Trajectories of Subjective Health: Testing Longitudinal Models for Self-rated Health From Adolescence to Midlife

Self-rated health (SRH) is ubiquitous in population health research. It is one of the few consistent health measures in longitudinal studies. Yet, extant research offers little guidance on its longitudinal trajectory. The literature on SRH suggests several possibilities, including SRH as (1) a more fixed, longer-term view of past, present, and anticipated health; (2) a spontaneous assessment at the time of the survey; (3) a result of lagged effects from prior responses; (4) a function of life course processes; and (5) a combination of the preceding. Different perspectives suggest different longitudinal models, but evidence is lacking about which model best captures SRH trajectory. Using data from the National Longitudinal Study of Adolescent to Adult Health and the National Longitudinal Survey of Youth, we employ structural equation modeling to correct for measurement error and identify the best-fitting, theoretically guided models describing SRH trajectories. Results support a hybrid model that combines the lagged effect of SRH with the enduring perspectives, fitted with a type of autoregressive latent trajectory (ALT) model. This model structure consistently outperforms other commonly used models and underscores the importance of accounting for lagged effects combined with time-invariant effects in longitudinal studies of SRH. Interestingly, comparisons of this latent, time-invariant autoregressive model across gender and racial/ethnic groups suggest that there are differences in starting points but less variability in SRH trajectories from early life into adulthood.

The Value of Limited Flexibility in Service Network Designs

Less-than-truckload carriers rely on the consolidation of freight from multiple shippers to achieve economies of scale. Collected freight is routed through a number of transfer terminals at each of which shipments are grouped together for the next leg of their journeys. We study the service network design problem confronted by these carriers. This problem includes determining (1) the number of services (trailers) to operate between each pair of terminals and (2) a load plan, which specifies the sequence of transfer terminals that freight with a given origin and destination will visit. Traditionally, for every terminal and every ultimate destination, a load plan specifies a unique next terminal. We introduce the [Formula: see text]-alt model, which generalizes traditional load plans by allowing decision makers to specify a desired number of next-terminal options for terminal–destination pairs using a vector [Formula: see text]. We compare a number of exact and heuristic approaches for solving a two-stage stochastic variant of the [Formula: see text]-alt model. Using this model, we show that, by explicitly considering demand uncertainty and by merely allowing up to two next-terminal options for terminal–destination pairs in the load plans, carriers can generate substantial cost savings that are comparable to the ones yielded by adopting load plans that allow for any next terminal to be a routing option for terminal–destination pairs. Moreover, by using these more flexible load plans, carriers can generate savings on the order of 10% over traditional load plan designs obtained by deterministic models.

Multisensory integration in the mouse cortical connectome using a network diffusion model

Having a structural network representation of connectivity in the brain is instrumental in analyzing communication dynamics and neural information processing. In this work, we make steps towards understanding multisensory information flow and integration using a network diffusion approach. In particular, we model the flow of evoked activity, initiated by stimuli at primary sensory regions, using the asynchronous linear threshold (ALT) diffusion model. The ALT model captures how evoked activity that originates at a given region of the cortex “ripples through” other brain regions (referred to as an activation cascade). We find that a small number of brain regions–the claustrum and the parietal temporal cortex being at the top of the list–are involved in almost all cortical sensory streams. This suggests that the cortex relies on an hourglass architecture to first integrate and compress multisensory information from multiple sensory regions, before utilizing that lower dimensionality representation in higher level association regions and more complex cognitive tasks.

Multi-sensory integration in the mouse cortical connectome using a network diffusion model

Having a structural network representation of connectivity in the brain is instrumental in analyzing communication dynamics and information processing in the brain. In this work, we make steps towards understanding multi-sensory information flow and integration using a network diffusion approach. In particular, we model the flow of evoked activity, initiated by stimuli at primary sensory regions, using the Asynchronous Linear Threshold (ALT) diffusion model. The ALT model captures how evoked activity that originates at a given region of the cortex “ripples through” other brain regions (referred to as an activation cascade). By comparing the model results to functional datasets based on Voltage Sensitive Dye (VSD) imaging, we find that in most cases the ALT model predicts the temporal ordering of an activation cascade correctly. Our results on the Mouse Connectivity Atlas from the Allen Institute for Brain Science show that a small number of brain regions are involved in many primary sensory streams – the claustrum and the parietal temporal cortex being at the top of the list. This suggests that the cortex relies on an hourglass architecture to first integrate and compress multi-sensory information from multiple sensory regions, before utilizing that lower-dimensionality representation in higher-level association regions and more complex cognitive tasks.

Optimum Time-Censored Modified Ramp-Stress ALT for the Burr Type XII Distribution with Warranty: A Goal Programming Approach

Accelerated life tests (ALTs) are used to make timely assessments of the life time distribution of highly reliable materials and components. Much of the previous work on ALTs has focused on constant-stress, step-stress, ramp-stress schemes and their various combinations. In the literature ramp-stress ALTs that have been formulated can be conducted when more than one test chambers are available; installation of which may prove to be costly. Even when one test chamber is used the stress rate remains constant throughout the duration of the experiment. It is not only necessary but imperative to examine if the test specimen is able to withstand differing stress conditions with the passage of time. This paper therefore, presents an optimal design of multi-objective modified ramp-stress ALT model with weighted goal programming approach. The modified ramp-stress uses one test chamber in place of the various chambers used in simple ramp-stress ALT thus saving experimental cost. With the market being increasingly competitive the emphasis today is on goal attainment with minimum deviations. Goal programming is a method to solve multi-objective problems that has applications in varied fields of engineering and operational research. The optimal plan consists in finding out relevant experimental variables, namely, stress rate, stress rate change point and warranty period, by using goal programming on weighted sum of variance of reliability function and expected warranty cost with pre-specified mission time under normal operating conditions. The Burr type XII life distribution and time-censored data have been used for the purpose. Burr type XII life distribution has been found appropriate for accelerated life testing experiments. The method developed has been explained using a numerical example and sensitivity analysis carried out. Comparative study has also been done to highlight the merits of the proposed model.

Sweat rate and prediction validation during high-altitude treks on Mount Kilimanjaro

This study measured sweat rates (msw) during high-altitude summer treks on Mt. Kilimanjaro to evaluate the efficacy of a recently developed fuzzy piecewise sweat prediction equation ( Ṗw,sol) for application to high-altitude conditions. We hypothesized that the Ṗw,sol equation, adjusted for the barometric pressure (Pb) decreasing steadily at high altitude ( Ṗw,sol+Alt), would allow for a more accurate prediction of msw than Ṗw,sol unadjusted for altitude ( Ṗw,solSL). Fifteen men (43 ± 16 yr; 80 ± 22 kg) and seven women (46 ± 16 yr; 77 ± 18 kg) wearing hiking clothes (clo ∼1.15; clothing evaporative potential = 0.27) and carrying light loads (9 ± 2 kg), were studied during morning and afternoon treks (∼2–3 h) while ascending from 2,829 m to 3,505 m. After each trek, msw was measured with specific biophysical parameters at 15-min intervals. During the trek day, Pb progressively declined (530 to 504 Torr), as solar radiation and ambient temperature (°C) rose transiently. During all treks, msw ranged from 68 to 393 g·m−2·h−1 (0.14 to 0.79 l/h). For each subject, derived Ṗw,solSL and Ṗw,sol+Alt model outputs accurately predicted the morning and afternoon average msw within a root mean square error of 0.145 l/h. No differences were found between Ṗw,solSL and Ṗw,sol+Alt values. In conclusion, we report the first msw measured during outdoor high-altitude activities and determined that Ṗw,solSL equation can be used to predict fluid needs during high-altitude activities without alterations for lower Pb. This model prediction provides a valid water planning tool for outdoor activities at high altitude up to 3,500 m.