Time-varying Rate Research Articles

Bletl - A Python package for integrating BioLector microcultivation devices in the Design-Build-Test-Learn cycle.

Microbioreactor (MBR) devices have emerged as powerful cultivation tools for tasks of microbial phenotyping and bioprocess characterization and provide a wealth of online process data in a highly parallelized manner. Such datasets are difficult to interpret in short time by manual workflows. In this study, we present the Python package bletl and show how it enables robust data analyses and the application of machine learning techniques without tedious data parsing and preprocessing. bletl reads raw result files from BioLector I, II and Pro devices to make all the contained information available to Python‐based data analysis workflows. Together with standard tooling from the Python scientific computing ecosystem, interactive visualizations and spline‐based derivative calculations can be performed. Additionally, we present a new method for unbiased quantification of time‐variable specific growth rate μ⃗t based on unsupervised switchpoint detection with Student‐t distributed random walks. With an adequate calibration model, this method enables practitioners to quantify time‐variable growth rate with Bayesian uncertainty quantification and automatically detect switch‐points that indicate relevant metabolic changes. Finally, we show how time series feature extraction enables the application of machine learning methods to MBR data, resulting in unsupervised phenotype characterization. As an example, Neighbor Embedding (t‐SNE) is performed to visualize datasets comprising a variety of growth/DO/pH phenotypes.

Effectiveness estimates of three COVID-19 vaccines based on observational data from Puerto Rico

BackgroundOn July 15, 2021, with 58% of the population fully vaccinated, the start of a COVID-19 surge was observed in Puerto Rico. On July 22, 2021, the government of Puerto Rico started imposing a series of strict vaccine mandates. Two months later, over 70% of the population was vaccinated, more than in any US state, and laboratory-confirmed SARS-CoV-2 had dropped substantially. The decision to impose mandates, as well as current Department of Health recommendations related to boosters, were guided by the data and the effectiveness estimates presented here.MethodsBetween December 15, 2020, when the vaccination process began in Puerto Rico, and October 15, 2021, 2,276,966 individuals were fully vaccinated against COVID-19. During this period 112,726 laboratory-confirmed SARS-CoV-2 infections were reported. These data permitted us to quantify the outcomes of the immunization campaign and to compare effectiveness of the mRNA-1273 (Moderna), BNT162b2 (Pfizer), and Ad26.COV2.S (J&J) vaccines. We obtained vaccination status, SARS-CoV-2 test results, and COVID-19 hospitalizations and deaths, from the Department of Health. We fit statistical models that adjusted for time-varying incidence rates and age group to estimate vaccine effectiveness, since the time of vaccination, against lab-confirmed SARS-CoV-2 infection, and COVID-19 hospitalization and death.ResultsTwo weeks after final dose, the mRNA-1273, BNT162b2, and Ad26.COV2.S vaccines had an effectiveness of 90% (95% CI: 88–91), 87% (85–88), and, 64% (58–69), respectively. After five months, effectiveness waned to about 70%, 50%, and 40%, respectively. We found no evidence that effectiveness was different after the Delta variant became dominant. For those infected, the vaccines provided further protection against COVID-19 hospitalization and deaths across all age groups, and this conditional effect did not wane in time.InterpretationThe mRNA-1273 and BNT162b2 vaccines were highly effective across all age groups. They were still effective after five months although the protection against SARS-CoV-2 infection waned. The Ad26.COV2.S vaccine was effective but to a lesser degree compared to the mRNA vaccines. Although, conditional on infection, protection against adverse outcomes did not wane, the waning in effectiveness resulted in a decreased protection against serious COVID-19 outcomes across time.FundingRAI's work was partly funded by NIH Grant R35GM131802.

The cooling of massive white dwarfs from Gaia EDR3

ABSTRACT We determine the distribution of cooling ages of massive Gaia EDR3 white dwarfs identified with over 90 per cent probability within 200 pc and with mass in the range 0.95–1.25 M⊙. Using three sets of publicly available models, we consider sub-samples of these white dwarfs sorted into three equally spaced mass bins. Under the assumption of a constant white dwarf formation rate, we find an excess of white dwarfs, both along the Q branch and below it, corresponding respectively to stars that are in the process of freezing and those that are completely frozen. We compare the cooling age distributions for each of these bins to the recently determined time-varying star formation rate of Gaia DR2 main sequence stars. For white dwarfs in the two lightest mass bins, spanning the mass range 0.95–1.15 M⊙, we find that the cumulative cooling age distribution is statistically consistent with the expectation from the star formation rate. For white dwarfs in the heaviest mass bin, 1.15–1.25 M⊙, we find that their cumulative distribution is inconsistent with the star formation rate for all of the models considered; instead, we find that their cooling age distribution is well fitted by a linear combination of the distribution expected for single stellar evolution products and the distribution expected for double white dwarf merger products when approximately 40–50 per cent of the 1.15–1.25 M⊙ white dwarfs that formed over the past 4 Gyr are produced through double white dwarf mergers.

Extended dissipativity and dynamical output feedback control for interval type-2 singular semi-Markovian jump fuzzy systems

This paper considers the problems of stochastic admissibility and extended dissipativity analysis, as well as dynamical output feedback controller design for interval type-2 singular semi-Markovian jump fuzzy systems. By utilising the partition method and the matrix decomposition technique, an improved approach of handling the time-varying transition rates is proposed. Based on this approach, a sufficient condition with less conservatism is established to guarantee that the closed-loop system is regular, impulse-free, exponentially stable in mean square and extended dissipative. Determining the parametric matrices of the controller via the obtained sufficient condition is difficult, since some parametric matrices factor between two nonsquare matrices. A dynamical output feedback control scheme is proposed by utilising a vital result which is proposed inspired by Projection lemma. It is worth noting that no conservatism is introduced in controller design due to the sufficiency and necessity of this result. Finally, simulation examples are given to show the effectiveness of the proposed scheme.

Robust Economic MPC of the Absorption Column in Post-Combustion Carbon Capture through Zone Tracking

Several studies have reported the importance of optimally operating the absorption column in a post-combustion CO2 capture (PCC) plant. It has been demonstrated in our previous work how economic model predictive control (EMPC) has a great potential to improve the operation of the PCC plant. However, the use of a general economic objective such as maximizing the absorption efficiency of the column can cause EMPC to drive the state of the system close to the constraints. This may lead to solvent overcirculation and flooding, which are undesirable. In this work, we present an EMPC with zone tracking algorithm as an effective means to address this problem. The proposed control algorithm incorporates a zone tracking objective and an economic objective to form a multi-objective optimal control problem. To ensure that the zone tracking objective is achieved in the presence of model uncertainties and time-varying flue gas flow rate, we propose a method to modify the original target zone with a control invariant set. The zone modification method combines both ellipsoidal control invariant set techniques and a back-off strategy. The use of ellipsoidal control invariant sets ensure that the method is applicable to large scale systems such as the absorption column. We present several simulation case studies that demonstrate the effectiveness and applicability of the proposed control algorithm to the absorption column in a post-combustion CO2 capture plant.

Does dynamic pricing work in a winter-peaking climate? A case study of Hydro Quebec

Ever since California experienced its energy crisis two decades ago, dynamic pricing of electricity has been the topic of discussion in numerous conferences and papers. Scores of pilots involving some 400 treatments of time-varying rates have been done to assess customer response to dynamic pricing around the globe, beginning with California’s Statewide Pricing Pilot that ran from 2003 to 04. In the past few years, we are witnessing large scale deployment of static time-varying rates in states such as California and Michigan. Colorado is about to embark on that journey. However, other than OG&E’s deployment of dynamic pricing, we have not seen much deployment of dynamic pricing in North America. California’s power outages in August 2002 have rekindled interest in the topic. But almost all of the discussion about dynamic pricing has focused on summer peaking utilities. In this paper, we discuss the experience of Hydro Quebec, a winter peaking utility in Canada. Hydro Quebec has tested both critical-peak pricing and peak-time rebates. The results are very encouraging and quite consistent with results from summer peaking utilities.The article is based on a question and answer format with Frederic Pelletier, who advises Hydro Quebec on tariff strategy. He had posted some results from their dynamic pricing deployment on LinkedIn in response to some results I had posted from three-pilots with TOU rates that had been carried out in Maryland. I put a few questions to Frederic and this article evolved out of that conversation. In the article that follows, the questions are mine and the answers are exclusively his.

The Laplace-transform embedded discrete fracture model for flow simulation of stimulated reservoir volume

Hydraulic fracturing is a commonly adopted approach to enhance the production of unconventional reservoirs; however, the resulted complex fracture network increases the difficulty in the prediction of the flow behaviors. In this work, for the first time, we propose the Laplace-transform embedded discrete fracture model focusing on the simulation of fluid transport in simulated reservoir volume (SRV). The main hydraulic fracture is represented explicitly by the fracture segments referring to the recently developed simulation scheme, namely the embedded discrete fracture model. The equivalent model and multiple continua model are employed to characterize the fluid flow in SRV. The Laplace-transform finite-different method is used to numerically model the flow among unstimulated reservoir volume, SRV, and main hydraulic fracture with sufficient flexibility to characterize the arbitrary fracture/SRV geometries. As the solution is performed in Laplace domain, the backward Euler difference for time discretization is not necessary. Thus, the stability and convergence problems caused by time discretization are avoided. A series of numerical test cases are discussed to examine the performance of the proposed model. The simulation workflow is validated through the comparison with discrete fracture model and embedded discrete fracture model in real space. It is demonstrated that the proposed Laplace-transform embedded discrete fracture model is accurate for single flow with complex SRV distribution. On the basis of the Laplace-transform embedded discrete fracture model, we provided three applications of the new method: (1) analysis of the SRV effect on fluid flow behavior, (2) pressure transient analysis considering reservoir heterogeneity, (3) production performance analysis with time-varying production rate.

Estimating and simulating a SIRD Model of COVID-19 for many countries, states, and cities

We use data on deaths in New York City, Madrid, Stockholm, and other world cities as well as in various U.S. states and other regions and countries to estimate, quickly and with limited data, a standard epidemiological model of COVID-19. We allow for a time-varying contact rate in order to capture behavioral and policy-induced changes associated with social distancing. We simulate the model forward to consider possible scenarios for various countries, states, and cities, including the potential impact of herd immunity on re-opening.

The Heterogeneous Severity of COVID-19 in African Countries: A Modeling Approach.

The COVID-19 pandemic has had a considerable impact on global health and economics. The impact in African countries has not been investigated thoroughly via fitting epidemic models to the reported COVID-19 deaths. We downloaded the data for the 12 most-affected countries with the highest cumulative COVID-19 deaths to estimate the time-varying basic reproductive number ({R}_{0}(t)) and infection attack rate. We develop a simple epidemic model and fitted it to reported COVID-19 deaths in 12 African countries using iterated filtering and allowing a flexible transmission rate. We observe high heterogeneity in the case-fatality rate across the countries, which may be due to different reporting or testing efforts. South Africa, Tunisia, and Libya were most affected, exhibiting a relatively higher {R}_{0}(t) and infection attack rate. Thus, to effectively control the spread of COVID-19 epidemics in Africa, there is a need to consider other mitigation strategies (such as improvements in socioeconomic well-being, healthcare systems, the water supply, and awareness campaigns).Supplementary InformationThe online version contains supplementary material available at 10.1007/s11538-022-00992-x.

Experimental study on radial gravity currents flowing in a vegetated channel

We present an experimental study of gravity currents in a cylindrical geometry, in the presence of vegetation. Forty tests were performed with a brine advancing in a fresh water ambient fluid, in lock release, and with a constant and time-varying flow rate. The tank is a circular sector of angle $30^\circ$ with radius equal to 180 cm. Two different densities of the vegetation were simulated by vertical plastic rods with diameter $D=1.6\ \textrm{cm}$ . We marked the height of the current as a function of radius and time and the position of the front as a function of time. The results indicate a self-similar structure, with lateral profiles that after an initial adjustment collapse to a single curve in scaled variables. The propagation of the front is well described by a power law function of time. The existence of self-similarity on an experimental basis corroborates a simple theoretical model with the following assumptions: (i) the dominant balance is between buoyancy and drag, parameterized by a power law of the current velocity $\sim |u|^{\lambda-1}u$ ; (ii) the current advances in shallow-water conditions; and (iii) ambient-fluid dynamics is negligible. In order to evaluate the value of ${\lambda}$ (the only tuning parameter of the theoretical model), we performed two additional series of measurements. We found that $\lambda$ increased from 1 to 2 while the Reynolds number increased from 100 to approximately $6\times10^3$ , and the drag coefficient and the transition from $\lambda=1$ to $\lambda=2$ are quantitatively affected by D, but the structure of the model is not.

High Taxes on Cloudy Days: Dynamic State-Induced Price Components in Power Markets

State-induced components constitute the major share of electricity prices for consumers and are generally charged at a fixed rate. We analyze the benefits of charging state-induced price components at time-varying rates instead, especially with regard to the integration of variable renewable energy (VRE) sources and to decarbonization. For this purpose, we apply a detailed power market model that puts particular emphasis on electricity demand and how it is sensitive to prices.For a quantitative case study, the model is parametrized to represent a German energy system with an 85% share of renewables in its power generation. We find that dynamization has a reducing effect on integration costs of VRE, ranging from 0.3 to 3.4 €/MWh. Furthermore, strong distributional effects in favor of flexible consumers can be observed.

Lyapunov stability of generalized ribosome flows*

In this paper we show that a wide class of compartmental systems with bounded capacities called generalized ribosome flow models are stable with an entropy-like logarithmic Lyapunov function known from the theory of nonnegative systems and reaction networks. The stability proof uses the kinetic representation of the compartmental model and earlier approaches applied for the input-to-state stability analysis of reaction networks with time-varying reaction rates. The results are valid not only for mass action type systems but also for models with more general reaction rates. Illustrative examples are given to show the qualitative dynamical properties of simple generalized ribosome flow models.

Effects of Varying Mass Inflows on Star Formation in Nuclear Rings of Barred Galaxies

Abstract Observations indicate that the star formation rate (SFR) of nuclear rings varies considerably with time and is sometimes asymmetric rather than being uniform across a ring. To understand what controls temporal and spatial distributions of ring star formation, we run semiglobal, hydrodynamic simulations of nuclear rings subject to time-varying and/or asymmetric mass inflow rates. These controlled variations in the inflow lead to variations in the star formation, while the ring orbital period (18 Myr) and radius (600 pc) remain approximately constant. We find that both the mass inflow rate and supernova feedback affect the ring SFR. An oscillating inflow rate with period Δτ in and amplitude 20 causes large-amplitude (a factor of ≳5), quasi-periodic variations of the SFR when Δτ in ≳ 50 Myr. We find that the time-varying interstellar medium (ISM) weight and midplane pressure track each other closely, establishing an instantaneous vertical equilibrium. The measured time-varying depletion time is consistent with the prediction from self-regulation theory provided the time delay between star formation and supernova feedback is taken into account. The supernova feedback is responsible only for small-amplitude (a factor of ∼2) fluctuations of the SFR with a timescale ≲40 Myr. Asymmetry in the inflow rate does not necessarily lead to asymmetric star formation in nuclear rings. Only when the inflow rate from one dust lane is suddenly increased by a large factor do the rings undergo a transient period of lopsided star formation.

Two waves of COIVD-19 in Brazilian cities and vaccination impact.

Brazil has suffered two waves of Coronavirus Disease 2019 (COVID-19). The second wave, coinciding with the spread of the Gamma variant, was more severe than the first wave. Studies have not yet reached a conclusion on some issues including the extent of reinfection, the infection fatality rate (IFR), the infection attack rate (IAR) and the effects of the vaccination campaign in Brazil, though it was reported that confirmed reinfection was at a low level. We modify the classical Susceptible-Exposed-Infectious-Recovered (SEIR) model with additional class for severe cases, vaccination and time-varying transmission rates. We fit the model to the severe acute respiratory infection (SARI) deaths, which is a proxy of the COVID-19 deaths, in 20 Brazilian cities with the large number of death tolls. We evaluate the vaccination effect by a contrast of "with" vaccination actual scenario and "without" vaccination in a counterfactual scenario. We evaluate the model performance when the reinfection is absent in the model. In the 20 Brazilian cities, the model simulated death matched the reported deaths reasonably well. The effect of the vaccination varies across cities. The estimated median IFR is around 1.2%. Overall, through this modeling exercise, we conclude that the effects of vaccination campaigns vary across cites and the reinfection is not crucial for the second wave. The relatively high IFR could be due to the breakdown of medical system in many cities.

A Synchronous Detection Algorithm for Quasi-Periodic Signal Components and Its Application in Photoplethysmographic Imaging

photoplethysmographic imaging (PPI) is a noninvasive method for assessing local blood flow beneath the skin surface by a video camera. The synchronization detection algorithm based on lock-in amplification (LIA) is a widely used method to obtain PPI. This method can obtain two maps: one is the amplitude map, which represents the strength of the PPG signal, and the other is the pulse-transit-time (PTT) map, which represents the time synchronization of the PPG signal. The latter is usually represented directly in form of a phase map. However, in general, the frequency of PPG signals is time-varying and contains harmonic components, which complicates the application of the algorithm. This article analyzes the relationship between the LIA calculation results and the time delay when the frequency of the signal is time-varying or the signal contains harmonics. Theoretical analysis results show that, when the signal contains harmonic components, the relationship between time delay and phase is approximately linear only when the time delay is not very large or the harmonic components are not very strong. Meanwhile, amplitude appears as a “bell-shaped” function, with its peak value located at the zero time delay. Video data were acquired by the video PPG acquisition system built in our laboratory, and the PPI was calculated by the synchronous detection algorithm using a reference signal generated directly by the Hilbert transform of an actual signal. Experiments show that the proposed method can obtain more stable results than using a constant-frequency reference signal when dealing with time-varying heart rate data. An interesting finding is that, when the reference signal contains harmonic components, PPG distribution features in the amplitude map can be enhanced.

A Switched Vertices Approach to Stability Analysis of Delayed Markov Jump Systems With Time-Varying Transition Rates

In the last five years, the stability of continuous-time Markov jump systems (MJSs) with time-varying transition rates (TRs) has been widely investigated by establishing parameter-dependent Lyapunov-Krasovskii functional (PDLKF), in which the time-varying TRs are considered to be in a polytopic sense. However, in the previous works, the derivative of polytopic parameters in PDLKF is assumed to be in a finite domain, which is limited in actual applications. In this brief, a switched vertices approach is proposed to remove the bounded assumption. On this basis, a new stability criterion is derived to guarantee the MJSs to be stochastically stable, which is more general and practical than the existing results. An example is illustrated to show the effectiveness of the obtained result.

Impact of advertisement on retailer’s inventory for time-varying perishable rate with preservation technology investment under customer return and trade credit policy

Impact of advertisement on retailer’s inventory for time-varying perishable rate with preservation technology investment under customer return and trade credit policy

{On Multiple Time-Varying Discount Rates with Recursive Time-Dependent Orders

{On Multiple Time-Varying Discount Rates with Recursive Time-Dependent Orders

Postdiagnosis BMI Change Is Associated with Non-Small Cell Lung Cancer Survival.

Body mass index (BMI) change after a lung cancer diagnosis has been associated with non-small cell lung cancer (NSCLC) survival. This study aimed to quantify the association based on a large-scale observational study. Included in the study were 7,547 patients with NSCLC with prospectively collected BMI data from Massachusetts General Hospital and Brigham and Women's Hospital/Dana-Farber Cancer Institute. Cox proportional hazards regression with time-dependent covariates was used to estimate effect of time-varying postdiagnosis BMI change rate (% per month) on overall survival (OS), stratified by clinical subgroups. Spline analysis was conducted to quantify the nonlinear association. A Mendelian Randomization (MR) analysis with a total of 3,495 patients further validated the association. There was a J-shape association between postdiagnosis BMI change and OS among patients with NSCLC. Specifically, a moderate BMI decrease [0.5-2.0; HR = 2.45; 95% confidence interval (CI), 2.25-2.67] and large BMI decrease (≥2.0; HR = 4.65; 95% CI, 4.15-5.20) were strongly associated with worse OS, whereas moderate weight gain (0.5-2.0) reduced the risk for mortality (HR = 0.78; 95% CI, 0.68-0.89) and large weight gain (≥2.0) slightly increased the risk of mortality without reaching statistical significance (HR = 1.10; 95% CI, 0.86-1.42). MR analyses supported the potential causal roles of postdiagnosis BMI change in survival. This study indicates that BMI change after diagnosis was associated with mortality risk. Our findings, which reinforce the importance of postdiagnosis BMI surveillance, suggest that weight loss or large weight gain may be unwarranted.

Neural networks for long-term earthquake prediction using modified meta-learning

Neural networks have been successfully applied for modeling time series. However, the results of long-term prediction are not satisfied. In this paper, the modified Meta-Learning is applied to the neural model. The normal Meta-Learning is modified by time-varying learning rates and adding a momentum term to improve convergence speed and robustness property. The stability of the learning process is proven. Finally, two experiments are presented to evaluate the proposed method. The first one shows an improvement in earthquakes prediction in the long-term, and the second one is a classical Benchmark problem. In both experiments, the modified Meta-Learning technique minimizes remarkably the mean square error index.