In this paper, we develop an adaptive control algorithm for addressing security for a class of networked vehicles that comprise a formation of
We study an inventory management mechanism that uses two stochastic programs (SPs), the customary one‐period assemble‐to‐order (ATO) model and its relaxation, to conceive control policies for dynamic ATO systems. We introduce a class of ATO systems, those that possess what we call a “chained BOM.” We prove that having a chained BOM is a sufficient condition for both SPs to be
- NSF-PAR ID:
- 10036894
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Production and Operations Management
- Volume:
- 26
- Issue:
- 3
- ISSN:
- 1059-1478
- Format(s):
- Medium: X Size: p. 446-468
- Size(s):
- ["p. 446-468"]
- Sponsoring Org:
- National Science Foundation
More Like this
-
Summary human‐driven vehicles sharing kinematic data and an autonomous vehicle in the aft of the vehicle formation receiving data from the preceding vehicles through wireless vehicle‐to‐vehicle communication devices. Specifically, we develop an adaptive controller for mitigating time‐invariant state‐dependent adversarial sensor and actuator attacks while guaranteeing uniform ultimate boundedness of the closed‐loop networked system. Furthermore, an adaptive learning framework is presented for identifying the state space model parameters based on input‐output data. This learning technique utilizes previously stored data as well as current data to identify the system parameters using a relaxed persistence of excitation condition. The effectiveness of the proposed approach is demonstrated by an illustrative numerical example involving a platoon of connected vehicles. -
New Findings What is the central question of this study? The respiratory centres in the brainstem that control respiration receive inputs from various sources, including proprioceptors in muscles and joints and suprapontine centres, which all affect limb movements. What is the effect of spontaneous movement on respiration in preterm infants?
What is the main finding and its importance? Apnoeic events tend to be preceded by movements. These activity bursts can cause respiratory instability that leads to an apnoeic event. These findings show promise that infant movements might serve as potential predictors of life‐threatening apnoeic episodes, but more research is required.
Abstract A common condition in preterm infants (<37 weeks’ gestational age) is apnoea resulting from immaturity and instability of the respiratory system. As apnoeas are implicated in several acute and long‐term complications, prediction of apnoeas may preempt their onset and subsequent complications. This study tests the hypothesis that infant movements are a predictive marker for apnoeic episodes and examines the relation between movement and respiration. Movement was detected using a wavelet algorithm applied to the photoplethysmographic signal. Respiratory activity was measured in nine infants using respiratory inductance plethysmography; in an additional eight infants, respiration and partial pressure of airway carbon dioxide (
) were measured by a nasal cannula with side‐stream capnometry. In the first cohort, the distribution of movements before and after the onset of 370 apnoeic events was compared. Results showed that apnoeic events were associated with longer movement duration occurring before apnoea onsets compared to after. In the second cohort, respiration was analysed in relation to movement, comparing standard deviation of inter‐breath intervals (IBI) before and after apnoeas. Poincaré maps of the respiratory activity quantified variability of airway in phase space. Movement significantly increased the variability of IBI and . Moreover, destabilization of respiration was dependent on the duration of movement. These findings support that bodily movements of the infants precede respiratory instability. Further research is warranted to explore the predictive value of movement for life‐threatening events, useful for clinical management and risk stratification. -
For decades, explorations with ground state, thermal reactions combined with pseudophase kinetic models and methods for interpreting the results have provided insights into the properties of the different regions of homogeneous association colloids. More recent successful determination of antioxidant (AO) distributions by this approach is providing new insights into AO efficiency in opaque, well‐mixed two‐phase intact emulsions and eliminating the need to separate the phases. The chemical probe reacts with AOs exclusively in the interfacial region of the emulsion, permitting simplification of the kinetic treatment, and determining its distribution between the oil, interfacial, and aqueous regions. AO distributions are obtained from the two partition constants,
and , of the AOs between the oil‐interfacial and aqueous‐interfacial regions, respectively. and values are obtained by fitting the observed rate constant, k obs, versus surfactant concentration profiles with an overall kinetic approach or model we call the “pseudophase chemical kinetic method.” However, because emulsions break up and reform, and reactants and other components diffuse at various time scales within and between the oil, interfacial, and water regions,k obscould also depend on reactant diffusion coefficients. Here we demonstrate that reactant diffusion is generally orders of magnitude faster than most thermal reactions and reactant distributions between the multiple oil, aqueous, and interfacial droplets and regions of emulsions are in dynamic equilibrium throughout the multiphase systems during the time course of the reaction. Thus, kinetic probes are powerful tools for determining structure‐reactivity, for example, the HLB, relationships governing AO distributions and efficiencies in emulsions.Practical applications: The analysis presented here demonstrates that one of the basic assumptions of the pseudophase chemical kinetic model that we have developed has a solid foundation in the properties of emulsions. That is, we can determine the distributions of reactants between oil (O), interfacial (I), and aqueous (W) regions of the emulsions because the diffusivity coefficients of reactants within emulsions are orders of magnitude greater than the rate of the reaction between the antioxidant and the 4‐hexadecylbenzenediazonium probe. Consequently, we can use the same kinetic model in emulsions as we have used in homogeneous microemulsions. The method permits determination of the partition constants of many antioxidants between the O‐I and W‐I regions of the emulsions and from them their distributions. The method provides new insights into the relationships between antioxidant hydrophobic‐lipophilic balance (HLB) and its efficiency in emulsions and a natural explanation for the cut‐off effect observed with increasing antioxidant HLB.Interpreting chemical reactivity in emulsions requires that reactive components
A andB be in dynamic equilibrium, that separate second order rate constants,k defined for the oil, interfacial, and aqueous regions, subscripts O, I, and W, and for simplicity, the volume of each region depends on the added volumes of oil, surfactant, and water. -
Abstract Technological advances have steadily increased the detail of animal tracking datasets, yet fundamental data limitations exist for many species that cause substantial biases in home‐range estimation. Specifically, the effective sample size of a range estimate is proportional to the number of observed range crossings, not the number of sampled locations. Currently, the most accurate home‐range estimators condition on an autocorrelation model, for which the standard estimation frame‐works are based on likelihood functions, even though these methods are known to underestimate variance—and therefore ranging area—when effective sample sizes are small.
Residual maximum likelihood (REML) is a widely used method for reducing bias in maximum‐likelihood (ML) variance estimation at small sample sizes. Unfortunately, we find that REML is too unstable for practical application to continuous‐time movement models. When the effective sample size
N is decreased toN ≤ (10), which is common in tracking applications, REML undergoes a sudden divergence in variance estimation. To avoid this issue, while retaining REML’s first‐order bias correction, we derive a family of estimators that leverage REML to make a perturbative correction to ML. We also derive AIC values for REML and our estimators, including cases where model structures differ, which is not generally understood to be possible. Using both simulated data and GPS data from lowland tapir (
Tapirus terrestris ), we show how our perturbative estimators are more accurate than traditional ML and REML methods. Specifically, when(5) home‐range crossings are observed, REML is unreliable by orders of magnitude, ML home ranges are ~30% underestimated, and our perturbative estimators yield home ranges that are only ~10% underestimated. A parametric bootstrap can then reduce the ML and perturbative home‐range underestimation to ~10% and ~3%, respectively. Home‐range estimation is one of the primary reasons for collecting animal tracking data, and small effective sample sizes are a more common problem than is currently realized. The methods introduced here allow for more accurate movement‐model and home‐range estimation at small effective sample sizes, and thus fill an important role for animal movement analysis. Given REML’s widespread use, our methods may also be useful in other contexts where effective sample sizes are small.
-
Triangular systems with nonadditively separable unobserved heterogeneity provide a theoretically appealing framework for the modeling of complex structural relationships. However, they are not commonly used in practice due to the need for exogenous variables with large support for identification, the curse of dimensionality in estimation, and the lack of inferential tools. This paper introduces two classes of semiparametric nonseparable triangular models that address these limitations. They are based on distribution and quantile regression modeling of the reduced form conditional distributions of the endogenous variables. We show that average, distribution, and quantile structural functions are identified in these systems through a control function approach that does not require a large support condition. We propose a computationally attractive three‐stage procedure to estimate the structural functions where the first two stages consist of quantile or distribution regressions. We provide asymptotic theory and uniform inference methods for each stage. In particular, we derive functional central limit theorems and bootstrap functional central limit theorems for the distribution regression estimators of the structural functions. These results establish the validity of the bootstrap for three‐stage estimators of structural functions, and lead to simple inference algorithms. We illustrate the implementation and applicability of all our methods with numerical simulations and an empirical application to demand analysis.