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The International Classification of Diseases (ICD) system includes sub codes to indicate that an individual with epilepsy is treatment resistant. These codes would be a valuable tool to identify individuals for quality improvement and population health, as well as for recruitment into clinical trials. However, the accuracy of these codes is unclear. We performed a single center cross sectional study to understand the accuracy of ICD codes for treatment resistant epilepsy. We identified 344 individuals, roughly half with treatment resistant epilepsy The ICD code had a sensitivity of 90% (147 of 164) and specificity of 86% (155 of 180). The miscoding of children with refractory epilepsy was attributed to the following reasons: 5 patients had epilepsy surgery, 4 had absence epilepsy, 4 patients were seen by different providers, and 1 patient was most recently seen in movement disorders clinic. ICD codes accurately identify children with treatment resistant epilepsy.

Clobazam is a commonly used long-acting benzodiazepine approved by the US Food and Drug Administration (FDA) to treat seizures associated with Lennox Gastaut syndrome. The FDA approved maximum dosage of clobazam is 1 mg/kg/d or a total of 40 mg a day. Many providers exceed this dosage but there is limited data on the safety, tolerability, and efficacy of supratherapeutic doses. We reviewed retrospective data at our institution and compared patients on supratherapeutic doses to patients on therapeutic doses. A total of 133 patients met inclusion criteria (65 supratherapeutic, 67 therapeutic). There was no statistically significant difference in terms of seizure control, health care utilization, or side effects between patients on supratherapeutic doses and those on therapeutic doses. This study lends further support to the safety and tolerability of supratherapuetic doses of clobazam.

Objectives: Seizure action plans help patients and caregivers better self-manage their epilepsy. We hypothesized that providing pediatric patients and their caregivers with a seizure action plan would reduce unplanned health care utilization and decrease the impact of epilepsy. Methods: We developed a seizure action plan for use in pediatric epilepsy patients. A prospective cohort was randomly assigned to receive a seizure action plan in addition to standard epilepsy care or to standard epilepsy care alone. All caregivers were surveyed using the Modified Impact on Families (MIF) questionnaire at enrollment, 3 months, and 12 months. Health care utilization measures and Modified Impact on Families questionnaire scores were compared between the 2 groups. Results: Fifty-four patients received a seizure action plan and standard care, whereas 48 received standard care alone. The groups had similar demographics. There was a significantly higher proportion of overall clinic appointment no shows in the standard care group vs the seizure action plan group ( P = .04); however, other significant differences in health care utilization were not found. Among patients with low seizure frequency (12 or fewer seizures per year), Seizure comfort scores on the Modified Impact on Families questionnaire were significantly higher at 12 months among the seizure action plan group compared to the standard care group. Significance: Caregivers for patients with epilepsy receiving a seizure action plan were more comfortable regarding seizure care and missed fewer appointments. However, differences in health care utilization were not present. The seizure action plan appears to have more impact in patients who experience lower seizure frequencies. Further studies evaluating the impact as well as assessing caregivers’ perceptions of the seizure action plan using a larger sample are needed.

Despite a very strong El Nino Southern Oscillation in 2015/2016, no coral mortality associated with bleaching was observed at the northern Galapagos (Ecuador) Islands of Darwin and Wolf. From March 2016 to March 2018, coral cover and health as well as water chemistry (NO3− and PO43−) and temperature were recorded. A marked heat anomaly reached 30 °C at Wolf in February 2016, but peak temperatures were attenuated after 2 d by a 4 °C drop. Temperature patterns at three depths (10, 15, and 20 m) and a subsequent and persistent phytoplankton bloom suggest topographically driven upwelling as the source of colder water and dissolved inorganic nutrients—both of which helped corals endure the heating episode. Consequently, no mortality and only partial bleaching were recorded in March 2016. Partially bleached corals contained numerous healthy zooxanthellae in deeper tissue layers. A continuous temperature record from 2012 to 2014 suggests that such upwelling events are common, inducing temperature fluctuations of up to 6 °C within 24 h during the observation period. Events at Wolf in 2016 suggest local upwelling reduced coral stress by relieving heat and by delivering nutrients required by corals to retain their regular temperature tolerance.

About 20 per 100,000 children have convulsive status epilepticus every year, a life-threatening condition. Benzodiazepines are the first-line treatment for prolonged and recurrent seizures. Our study was designed to gain understanding of caregiver perception of acute seizure treatments.Our project uses a cross-sectional survey study design using the electronic medical record and a survey at a large academic tertiary children's medical center. Subjects were patients with epilepsy prescribed intranasal (IN) midazolam and/or per rectum (PR) diazepam. The survey was administered to caregivers of children with epilepsy regarding information on the comfort, efficacy, ease of use, and time of administration for patients receiving both abortive seizure medications. Exact binomial tests were employed to determine whether or not differences in caregiver preference exist.One hundred and sixty responses were obtained. Incomplete and duplicate surveys were excluded, leaving 153 responses. Of those responses, 59 respondents reported administering both medications. Among parents who expressed a preference for one medication over the other, more parents felt overall greater comfort with IN midazolam compared with rectal diazepam (p = 0.0004 and p = 0.001), IN midazolam was perceived as easier to use (68%, p = 0.0038 and 74%, p = 0.0004) and more effective (87%, p 0.0001) than rectal diazepam. Intranasal midazolam was found to be superior to rectal diazepam in several other categories as well.These parents of children with epilepsy report increased ease of use, comfort, and efficacy with IN midazolam as compared with rectal diazepam suggesting that a readily available form of IN midazolam would be well received in the pediatric population.

Decarbonization of electricity generation together with electrification of energy-and-carbon-intensive services such as heating and cooling is needed to address ambitious climate goals. Here we show that city-scale electrification of heat with large-scale thermal storage also cost-effectively unlocks significant additional operational benefits for the power sector. We build an optimization model of fully electrified district heating and cooling networks integrated with other electric loads. We leverage real-world consumption and operational data from a first-of-a-kind facility that meets heating, cooling and electrical energy requirements equivalent to a city of 30 000 people. Using our model, we compute optimal operational strategies for the controllable loads and thermal storage in this system under different economic hypotheses. In our example, electrifying the previously gas-based heating and cooling infrastructure has led to a 65% reduction in the overall campus carbon footprint. Through least-cost scheduling, the load shape of the aggregate energy system can be flattened and annual peak power demand can be reduced by 15%. Through carbon-aware scheduling that takes advantage of variations in grid power carbon intensity, heating and cooling emissions could further decrease by over 40% in 2025 compared to the 2016 baseline, assuming a policy-compliant electricity mix for California. However, rethinking electricity rates based on peak power usage will be needed to make carbon-aware scheduling economically attractive.

Widespread and large populations of the exotic eastern Pacific ophiuroid brittle star Opthiothela mirabilis now occur in southeastern Florida, extending the range of this recently introduced species from southern Brazil northward to the eastern Caribbean Sea and Florida. The Florida brittle stars, representing two lineages, are epibionts on shallow (3-18 m depth), tropical/subtropical plexaurid (e.g., Eunicea spp., Muricea elongata) and gorgoniid (Antillogorgia spp.) octocorals. The scope of this study includes recent distributional records of O. mirabilis in south Florida, field abundances in relation to the cohabiting endemic ctenophore Coeloplana waltoni, behavioral observations of the ophiuroid, ctenophore and the predatory amphipod Caprella penantis, as well as a laboratory experiment testing the effects of the alien ophiuroid on the native ctenophore. Individuals of O. mirabilis have been collected near St. Lucie Inlet, extending its northern-most range by about 110 km since 2019. Two years of field sampling have demonstrated significant declines of the native, benthic ctenophore with increasing abundances of the exotic ophiuroid. Evidence suggests that the ophiuroid is negatively affecting the abundances of the ctenophore through interference competition, greatly aided by its abrasive armature of calcareous spines, plates and hooks. Sporadic and intense predation by a caprellid amphipod (Caprella penantis) also probably contributes to the ctenophore’s decline, but to a lesser extent than that caused by the ophiuroid. Adding to the risk of extinction of C. waltoni is its narrow requirement of living octocorals as hosts and restricted distribution in southeast Florida and the Bahamas.

Distortion risk measure, defined by an integral of a distorted tail probability, has been widely used in behavioral economics and risk management as an alternative to expected utility. The sensitivity of the distortion risk measure is a functional of certain distribution sensitivities. We propose a new sensitivity estimator for the distortion risk measure that uses generalized likelihood ratio estimators for distribution sensitivities as input and establish a central limit theorem for the new estimator. The proposed estimator can handle discontinuous sample paths and distortion functions.

One of the most widely used methods for solving large-scale stochastic optimization problems is distributed asynchronous stochastic gradient descent (DASGD), a family of algorithms that result from parallelizing stochastic gradient descent on distributed computing architectures (possibly) asychronously. However, a key obstacle in the efficient implementation of DASGD is the issue of delays: when a computing node contributes a gradient update, the global model parameter may have already been updated by other nodes several times over, thereby rendering this gradient information stale. These delays can quickly add up if the computational throughput of a node is saturated, so the convergence of DASGD may be compromised in the presence of large delays. Our first contribution is that, by carefully tuning the algorithm's step-size, convergence to the critical set is still achieved in mean square, even if the delays grow unbounded at a polynomial rate. We also establish finer results in a broad class of structured optimization problems (called variationally coherent), where we show that DASGD converges to a global optimum with probability $1$ under the same delay assumptions. Together, these results contribute to the broad landscape of large-scale non-convex stochastic optimization by offering state-of-the-art theoretical guarantees and providing insights for algorithm design., 41 pages, 8 figures; to be published in Mathematics of Operations Research

submitted; International audience; We consider the target-rate power management problem for wireless networks and we propose two simple, distributed power management schemes that regulate power in a provably robust manner by efficiently leveraging past information. Both schemes are obtained via a combined approach of learning and “game design” where we (1) design a game with suitable payoff functions such that the optimal joint power profile in the original power management problem is the unique Nash equilibrium of the designed game; and (2) derive distributed power managment algorithms by directing the networks' users to employ a no-regret learning algorithm to maximize their individual utility over time. To establish convergence, we focus on the well- known online eager gradient descent learning algorithm in the class of weighted strongly monotone games. In this class of games, we show that when players only have access to imperfect stochastic feedback, multi-agent online eager gradient descent converges to the unique Nash equilibrium in mean square at an $\mathcal{O}(1/T)$ rate. In the context of power management in static networks, we show that the designed games are weighted strongly monotone if the network is feasible (i.e. when all users can concurrently attain their target rates). This allows us to derive geometric convergence rate to the joint optimal transmission power. More importantly, in stochastic networks where channel quality fluctuates over time, the designed games are also weighted strongly monotone and the proposed algorithms converge in mean square to the joint optimal transmission power at a $\mathcal{O}(1/T)$ rate, even when the network is only feasible on average (i.e. users may be unable to meet their requirements with positive probability). This comes in stark contrast to existing algorithms (like the seminal Foschini–Miljanic algorithm and its variants) that may fail to converge altogether.

Weather forecast information will very likely find increasing application in the control of future energy systems. In this paper, we introduce an augmented state space model formulation with linear dynamics, within which one can incorporate forecast information that is dynamically revealed alongside the evolution of the underlying state variable. We use the martingale model for forecast evolution (MMFE) to enforce the necessary consistency properties that must govern the joint evolution of forecasts with the underlying state. The formulation also generates jointly Markovian dynamics that give rise to Markov decision processes (MDPs) that remain computationally tractable. This paper is the first to enforce MMFE consistency requirements within an MDP formulation that preserves tractability., Comment: Accepted manuscript in an upcoming special issue of the Philosophical Transactions of the Royal Society A on the Mathematics of Energy Systems

Optimal transport costs (e.g. Wasserstein distances) are used for fitting high-dimensional distributions. For example, popular artificial intelligence algorithms such as Wasserstein Generative Adversarial Networks (WGANs) can be interpreted as fitting a black-box simulator of structured data with certain features (e.g. images) using the Wasserstein distance. We propose a regularization of optimal transport costs and study its computational and duality properties. We obtain running time improvements for fitting WGANs with no deterioration in testing performance, relative to current benchmarks. We also derive finite sample bounds for the empirical Wasserstein distance from our regularization.

Simulation is often used to evaluate and compare performances of stochastic systems, where the underlying stochastic models are estimated from real-world input data. Collecting more input data can derive closer-to-reality stochastic models while generating more simulation replications can reduce stochastic errors. With the objective of selecting the system with the best performance, we propose a general framework to analyze the joint resource allocation problem for collecting input data and generating simulation replications. Two commonly arised features, correlation in input data and common random numbers in simulation, are jointly exploited to save cost and enhance efficiency. In presence of both features, closed-form joint resource allocation solutions are given for the comparison of two systems.

We consider the estimation of the distribution of the hitting time to a rarely visited set of states for a regenerative process. In a previous paper, we provided two estimators that exploited the weak convergence of the hitting time divided by its expectation to an exponential as the rare set becomes rarer. We now add three new estimators, based on a corrected exponential, a gamma, and a bootstrap approach, the last possibly providing less biased estimators when the rare set is only moderately rare. Numerical results illustrate that all of the estimators perform similarly. Although the paper focuses on estimating a distribution, the ideas can also be applied to estimate risk measures, such as a quantile or conditional tail expectation.

An unequivocal link exists between human population density and environmental degradation, both in the near field (local impacts) and far field (impacts due to teleconnections). Human population is most widely predicted to reach 9-11 billion by 2100, when the demographic transition is expected in all but a handful of countries. Strongest population growth is in the tropics, where coral reefs face dense human population and concomitant heavy usage. In most countries,50% will be urbanized but growth of rural population and need for food in urban centres will not alleviate pressure on reef resources. Aquaculture will alleviate some fishing pressure, but still utilizes reef surface and is also destructive. Denser coastal populations and greater wealth will lead to reef degradation by coastal construction. Denser populations inland will lead to more runoff and siltation. Effects of human perturbations can be explored with metapopulation theory since they translate to increases in patch-mortality and decreases in patch-colonization (=regeneration). All such changes will result in a habitat with overall fewer settled patches, so fewer live reefs. If rescue effects are included, bifurcations in system dynamics will allow for many empty patches and, depending on system state relative to stable and unstable equilibria, a part-empty system may either trend towards stability at higher patch occupancy or extinction. Thus, unless the disturbance history is known, it may be difficult to assess the direction of system trajectory-making management difficult. If habitat is decreased by destruction, rescue effects become even more important as extinction-debt, accumulated by efficient competitors with weaker dispersal ability, is realized. Easily visible trends in human population dynamics combined with well-established and tested ecological theory give a clear, intuitive, yet quantifiable guide to the severity of survival challenges faced by coral reefs. Management challenges and required actions can be clearly shown and, contrary to frequent claims, no scientific ambiguity exists with regards to the serious threat posed to coral reefs by humankind's continued numerical increase.

The invasive brittle star Ophiothela mirabilis (family Ophiotrichidae), a tropical Indo-Pacific endemic species, first reported in Atlantic waters off southern Brazil in 2000, has extended its range northward to the Caribbean Sea, to the Lesser Antilles in 2011, and was first reported in south Florida in January 2019. Its occurrence in southeast Florida extends along nearly 70km of coastline, from near the Port of Miami, Miami-Dade County, northward to Deerfield Beach, Broward County. It occurs abundantly as an epizoite on octocorals, attaining population densities of 25 individuals and more per 10-cm long octocoral stem. The surface texture of octocoral hosts (rough, smooth) did not affect the densities of the ophiuroid epizoites, and there were significantly greater abundances on octocorals during two winter sampling periods than in the summer. Beige and orange-coloured morphs are sometimes present on the same octocoral stem. Gut content analysis supported a suspension feeding mode, revealing essentially identical ingested items in both colour morphs with a preponderance of amorphous detritus and filamentous algae. Molecular genetic evidence (COI16s) has established the identity of O. mirabilis and its relationship to invasive Brazilian populations. The orange and beige morphs form two distinct, but closely related lineages that may represent two separate introductions. The orange morph shares haplotypes with Brazilian and Caribbean specimens suggesting a further range expansion of the 'original' invasion. The beige morph, however, shares haplotypes with specimens from the Mexican Pacific and Peru and potentially represents a secondary introduction. Traits promoting dispersal and establishment of this species in new habitats are manifold: vagility and ability to cling tightly to diverse host taxa (e.g. sponges, cnidarians, bryozoans, and echinoderms), frequent asexual reproduction (fissiparity), suspension feeding, including a wide range of dietary items, possession of integument-covered ossicles and arm spines offering protection from predators, and an effective competitive edge over associated microbiota for substrate space.

Objective: To evaluate the impact of a pediatric epilepsy care management intervention on emergency department visits, hospitalizations, and seizure freedom. Methods: We conducted a prospective observational study at a single academic medical center. Children with epilepsy with high risk of frequent emergency department use were enrolled in the intervention from January through May 2015, which included a baseline visit and follow-up support from a care management team. Controls selected from the same institution received standard of care. Baseline and follow-up information were collected from electronic health records and surveys (Family Impact Scale, Pediatric Epilepsy Medication Self-Management Questionnaire). Propensity score–weighted logistic regression compared emergency department visits, unplanned hospitalizations, and 3-month seizure freedom after 1 year in the intervention vs control groups. Results: A total of 56 children were enrolled in the intervention and 359 received standard of care. The intervention group was younger and had greater use of health services at baseline. When comparing the intervention to standard of care after 1 year, we found no significant difference in the risk of any emergency department visit (adjusted odds ratio [OR] 2.2, 95% confidence interval [CI] 0.6-8.5) or seizure freedom (adjusted OR 2.5, 95% CI 0.3-21.5). However, the risk of unplanned hospital admissions remained higher in the intervention group (adjusted OR 23.1, 95% CI 5.1-104). Conclusion: We did not find that children with epilepsy who received a care management intervention had less use of health services or better clinical outcomes after a year compared with controls. The study is limited by small sample size and nonrandomized study design.

Currently, the tracking of seizures is highly subjective, dependent on qualitative information provided by the patient and family instead of quantifiable seizure data. Usage of a seizure detection device to potentially detect seizure events in a population of epilepsy patients has been previously done. Therefore, we chose the Fitbit Charge 2 smart watch to determine if it could detect seizure events in patients when compared to continuous electroencephalographic (EEG) monitoring for those admitted to an epilepsy monitoring unit. A total of 40 patients were enrolled in the study that met the criteria between 2015 and 2016. All seizure types were recorded. Twelve patients had a total of 53 epileptic seizures. The patient-aggregated receiver operating characteristic curve had an area under the curve of 0.58 [0.56, 0.60], indicating that the neural network models were generally able to detect seizure events at an above-chance level. However, the overall low specificity implied a false alarm rate that would likely make the model unsuitable in practice. Overall, the use of the Fitbit Charge 2 activity tracker does not appear well suited in its current form to detect epileptic seizures in patients with seizure activity when compared to data recorded from the continuous EEG.

Background:We sought to build an accessible interactive model that could facilitate hospital capacity planning in the presence of significant uncertainty about the proportion of the population that is positive forcoronavirus disease 2019 (COVID-19) and the rate at which COVID-19 is spreading in the population. Our goal was to facilitate the implementation of data-driven recommendations for capacity management with a transparent mathematical simulation designed to answer the specific, local questions hospital leadership considered critical.Methods:The model facilitates hospital planning with estimates of the number of Intensive Care (IC) beds, Acute Care (AC) beds, and ventilators necessary to accommodate patients who require hospitalization for COVID-19 and how these compare to the available resources. Inputs to the model include estimates of the characteristics of the patient population and hospital capacity. We deployed this model as an interactive online tool with modifiable parameters.Results:The use of the model is illustrated by estimating the demand generated by COVID-19+ arrivals for a hypothetical acute care medical center. The model calculated that the number of patients requiring an IC bed would equal the number of IC beds on Day 23, the number of patients requiring a ventilator would equal the number of ventilators available on Day 27, and the number of patients requiring an AC bed and coverage by the Medicine Service would equal the capacity of the Medicine service on Day 21. The model was used to inform COVID-19 planning and decision-making, including Intensive Care Unit (ICU) staffing and ventilator procurement.Conclusion:In response to the COVID-19 epidemic, hospitals must understand their current and future capacity to care for patients with severe illness. While there is significant uncertainty around the parameters used to develop this model, the analysis is based on transparent logic and starts from observed data to provide a robust basis of projections for hospital managers. The model demonstrates the need and provides an approach to address critical questions about staffing patterns for IC and AC, and equipment capacity such as ventilators.Contributions to the literature:· Generation and implementation of data-driven recommendations for hospital capacity management early in the COVID-19 pandemic· The conceptualization, development, and deployment of an interactive simulation model in two weeks· Data-driven capacity management in the presence of significant uncertainty about the expected volume of patients, their clinical needs, and the availability of the workforceTrial Registration: Not applicable

COVID-19 threatens to overwhelm hospital facilities throughout the United States. We created an interactive, quantitative model that forecasts demand for COVID-19 related hospitalization based on county-level population characteristics, data from the literature on COVID-19, and data from online repositories. Using this information as well as user inputs, the model estimates a time series of demand for intensive care beds and acute care beds as well as the availability of those beds. The online model is designed to be intuitive and interactive so that local leaders with limited technical or epidemiological expertise may make decisions based on a variety of scenarios. This complements high-level models designed for public consumption and technically sophisticated models designed for use by epidemiologists. The model is actively being used by several academic medical centers and policy makers, and we believe that broader access will continue to aid community and hospital leaders in their response to COVID-19.LINK TO ONLINE MODELhttps://surf.stanford.edu/covid-19-tools/covid-19/

As of March 23, 2020 there have been over 354,000 confirmed cases of coronavirus disease 2019 (COVID-19) in over 180 countries, the World Health Organization characterized COVID-19 as a pandemic, and the United States (US) announced a national state of emergency.1, 2, 3 In parts of China and Italy the demand for intensive care (IC) beds was higher than the number of available beds.4, 5 We sought to build an accessible interactive model that could facilitate hospital capacity planning in the presence of significant uncertainty about the proportion of the population that is COVID-19+ and the rate at which COVID-19 is spreading in the population. Our approach was to design a tool with parameters that hospital leaders could adjust to reflect their local data and easily modify to conduct sensitivity analyses.We developed a model to facilitate hospital planning with estimates of the number of Intensive Care (IC) beds, Acute Care (AC) beds, and ventilators necessary to accommodate patients who require hospitalization for COVID-19 and how these compare to the available resources. Inputs to the model include estimates of the characteristics of the patient population and hospital capacity. We deployed this model as an interactive online tool.6 The model is implemented in R 3.5, RStudio, RShiny 1.4.0 and Python 3.7. The parameters used may be modified as data become available, for use at other institutions, and to generate sensitivity analyses.We illustrate the use of the model by estimating the demand generated by COVID-19+ arrivals for a hypothetical acute care medical center. The model calculated that the number of patients requiring an IC bed would equal the number of IC beds on Day 23, the number of patients requiring a ventilator would equal the number of ventilators available on Day 27, and the number of patients requiring an AC bed and coverage by the Medicine Service would equal the capacity of the Medicine service on Day 21.In response to the COVID-19 epidemic, hospitals must understand their current and future capacity to care for patients with severe illness. While there is significant uncertainty around the parameters used to develop this model, the analysis is based on transparent logic and starts from observed data to provide a robust basis of projections for hospital managers. The model demonstrates the need and provides an approach to address critical questions about staffing patterns for IC and AC, and equipment capacity such as ventilators.

We propose and study an asymptotically optimal Monte Carlo estimator for steady-state expectations of a d-dimensional reflected Brownian motion (RBM). Our estimator is asymptotically optimal in the sense that it requires [Formula: see text] (up to logarithmic factors in d) independent and identically distributed scalar Gaussian random variables in order to output an estimate with a controlled error. Our construction is based on the analysis of a suitable multilevel Monte Carlo strategy which, we believe, can be applied widely. This is the first algorithm with linear complexity (under suitable regularity conditions) for a steady-state estimation of RBM as the dimension increases.

In this paper, we examine the convergence of mirror descent in a class of stochastic optimization problems that are not necessarily convex (or even quasi-convex), and which we call variationally coherent. Since the standard technique of "ergodic averaging" offers no tangible benefits beyond convex programming, we focus directly on the algorithm's last generated sample (its "last iterate"), and we show that it converges with probabiility $1$ if the underlying problem is coherent. We further consider a localized version of variational coherence which ensures local convergence of stochastic mirror descent (SMD) with high probability. These results contribute to the landscape of non-convex stochastic optimization by showing that (quasi-)convexity is not essential for convergence to a global minimum: rather, variational coherence, a much weaker requirement, suffices. Finally, building on the above, we reveal an interesting insight regarding the convergence speed of SMD: in problems with sharp minima (such as generic linear programs or concave minimization problems), SMD reaches a minimum point in a finite number of steps (a.s.), even in the presence of persistent gradient noise. This result is to be contrasted with existing black-box convergence rate estimates that are only asymptotic., Comment: 30 pages, 5 figures

An unequivocal link exists between human population density and environmental degradation, both in the near field (local impacts) and far field (impacts due to teleconnections). Human population is most widely predicted to reach 9–11 billion by 2100, when the demographic transition is expected in all but a handful of countries. Strongest population growth is in the tropics, where coral reefs face dense human population and concomitant heavy usage. In most countries, > 50% will be urbanized but growth of rural population and need for food in urban centres will not alleviate pressure on reef resources. Aquaculture will alleviate some fishing pressure, but still utilizes reef surface and is also destructive. Denser coastal populations and greater wealth will lead to reef degradation by coastal construction. Denser populations inland will lead to more runoff and siltation. Effects of human perturbations can be explored with metapopulation theory since they translate to increases in patch-mortality and decreases in patch-colonization (= regeneration). All such changes will result in a habitat with overall fewer settled patches, so fewer live reefs. If rescue effects are included, bifurcations in system dynamics will allow for many empty patches and, depending on system state relative to stable and unstable equilibria, a part-empty system may either trend towards stability at higher patch occupancy or extinction. Thus, unless the disturbance history is known, it may be difficult to assess the direction of system trajectory—making management difficult. If habitat is decreased by destruction, rescue effects become even more important as extinction-debt, accumulated by efficient competitors with weaker dispersal ability, is realized. Easily visible trends in human population dynamics combined with well-established and tested ecological theory give a clear, intuitive, yet quantifiable guide to the severity of survival challenges faced by coral reefs. Management challenges and required actions can be clearly shown and, contrary to frequent claims, no scientific ambiguity exists with regards to the serious threat posed to coral reefs by humankind's continued numerical increase.

The invasive brittle star Ophiothela mirabilis (family Ophiotrichidae), a tropical Indo-Pacific endemic species, first reported in Atlantic waters off southern Brazil in 2000, has extended its range northward to the Caribbean Sea, to the Lesser Antilles in 2011, and was first reported in south Florida in January 2019. Its occurrence in southeast Florida extends along nearly 70 km of coastline, from near the Port of Miami, Miami-Dade County, northward to Deerfield Beach, Broward County. It occurs abundantly as an epizoite on octocorals, attaining population densities of 25 individuals and more per 10-cm long octocoral stem. The surface texture of octocoral hosts (rough, smooth) did not affect the densities of the ophiuroid epizoites, and there were significantly greater abundances on octocorals during two winter sampling periods than in the summer. Beige and orange-coloured morphs are sometimes present on the same octocoral stem. Gut content analysis supported a suspension feeding mode, revealing essentially identical ingested items in both colour morphs with a preponderance of amorphous detritus and filamentous algae. Molecular genetic evidence (COI & 16s) has established the identity of O. mirabilis and its relationship to invasive Brazilian populations. The orange and beige morphs form two distinct, but closely related lineages that may represent two separate introductions. The orange morph shares haplotypes with Brazilian and Caribbean specimens suggesting a further range expansion of the ‘original’ invasion. The beige morph, however, shares haplotypes with specimens from the Mexican Pacific and Peru and potentially represents a secondary introduction. Traits promoting dispersal and establishment of this species in new habitats are manifold: vagility and ability to cling tightly to diverse host taxa (e.g. sponges, cnidarians, bryozoans, and echinoderms), frequent asexual reproduction (fissiparity), suspension feeding, including a wide range of dietary items, possession of integument-covered ossicles and arm spines offering protection from predators, and an effective competitive edge over associated microbiota for substrate space.

Power control over wireless networks has been an active area of research with significant applied impact. A well-motivated line of this research, which has received increasing attention, is applying game-theoretic tools for both gaining insight and design of algorithms. In this paper, we build on the existing work and present a simple game-theoretic formulation of power control on wireless networks that incorporates two novel features. First, we do not impose exogenous power bounds on the feasible transmission power. Second, we allow the channel environment to be stochastic and time varying. Within this model, we first examine the deterministic game under a fixed environment, in which we develop a novel fixed-point theorem of independent interest that operates in general and unbounded partially ordered sets. We then leverage this customized fixed-point theorem to establish various equilibrium-related results: existence, uniqueness, and convergence, followed by a novel Price-of-Anarchy bound characterization. Finally, we study the stochastic behavior of the best response dynamics and establish a number of desirable properties in the presence of a stochastic and time-varying channel.

This paper studies the rate of convergence to equilibrium for two diffusion models that arise naturally in the queueing context: two-sided reflected Brownian motion and the Ornstein–Uhlenbeck process. Specifically, we develop exact asymptotics and upper bounds on total variation distance to equilibrium, which can be used to assess the quality of the steady state as an approximation to finite-horizon performance quantities. Our analysis relies upon the simple spectral structure that these two processes possess, thereby explaining why the convergence rate is “pure exponential,” in contrast to the more complex convergence exhibited by one-sided reflected Brownian motion.

To evaluate the impact of receiving a wish from the Make-A-WishR Foundation on (1) patient healthcare utilization and (2) savings benefit measures. Make-A-WishR arranges experiences, or “wishes,” to children with progressive, life-threatening, or life-limiting illness. A retrospective, case–control analysis was performed comparing patients who received or did not receive a wish and associated impact on healthcare utilization and costs across 2 years. Healthcare utilization was defined as visits to primary, urgent, emergent care, and planned/unplanned inpatient hospitalizations. We defined wish savings benefit as a decline in the cost of care from years 1 to 2, which exceeded the average cost of a wish in 2016, $10,130. From 2011 to 2016, 496 Nationwide Children’s Hospital patients received a wish. We matched these patients to 496 controls based on age, gender, disease category, and disease complexity. Patients who received a wish were 2.5 and 1.9 times more likely to have fewer unplanned hospital admissions and emergency department visits, respectively. These decreases were associated with a higher likelihood (2.3-fold and 2.2-fold greater odds) of the wish achieving a savings benefit compared to hospital charges. Participation in the Make-A-WishR program may provide children quality of life relief while reducing hospital visits and healthcare expenditures.

We establish the asymptotic validity of a class of sequential stopping rules when applying standardized time series (STS) to construct fixed-width confidence intervals (CI). The STS CI construction avoids requiring a consistent variance estimator, which is attractive to a class of steady-state simulation problems in which variance estimation is difficult. We quantify the asymptotic distribution of STS at stopping times as the prescribed half-width of the CI approaches zero. This provides us with the appropriate scaling parameter for the CI in the sequential stopping setting.

RECURRENCE CLASSIFICATION FOR A FAMILY OF NON-LINEAR STORAGE MODELSNecessary and sufficient conditions for positive recurrence of a discrete-time non-linear storage model with power law dynamics arederived. In addition, necessary and sufficient conditions for finiteness of p-th stationary moments are obtained for this class of models.

This paper discusses the rate of convergence to equilibrium for one-dimensional reflected Brownian motion with negative drift and lower reflecting boundary at 0. In contrast to prior work on this problem, we focus on studying the rate of convergence for the entire distribution through the total variation norm, rather than just moments of the distribution. In addition, we obtain computable bounds on the total variation distance to equilibrium that can be used to assess the quality of the steady state for queues as an approximation to finite horizon expectations.

We consider the context of “simulation-based recursions,” that is, recursions that involve quantities needing to be estimated using a stochastic simulation. Examples include stochastic adaptations of fixed-point and gradient descent recursions obtained by replacing function and derivative values appearing within the recursion by their Monte Carlo counterparts. The primary motivating settings are simulation optimization and stochastic root finding problems, where the low point and the zero of a function are sought, respectively, with only Monte Carlo estimates of the functions appearing within the problem. We ask how much Monte Carlo sampling needs to be performed within simulation-based recursions in order that the resulting iterates remain consistent and, more importantly, efficient, where “efficient” implies convergence at the fastest possible rate. Answering these questions involves trading off two types of error inherent in the iterates: the deterministic error due to recursion and the “stochastic” er...

Two benthic ctenophores, Coeloplana waltoni and Vallicula multiformis, are contrasted in terms of their coastal environments, habitats, abundances, seasonal occurrences, and behavior in south Florida. Coeloplana waltoni occurs as an epibiont on octocorals in open water settings, and V. multiformis is present in bio-fouling communities, associated with macroalgae, bryozoans, and inanimate substrates in protected, back-water habitats. In our study, individuals of C. waltoni were found under moderate to strong current flow and relatively constant temperature and salinity (T/S) conditions, whereas individuals of V. multiformis occurred at sites of low current flow and more variable T/S conditions. In C. waltoni, individuals generally adhered tightly to host colony surfaces, whereas in V. multiformis, individuals often disassociated from substrates and floated free. Mean population densities of C. waltoni ranged ~500–850 individuals 100 mL−1 (measured as the displacement volume of the octocoral habitat), and densities of V. multiformis ranged 5–360 individuals 100 mL−1 (measured as the displacement volume of the biofouling habitat). Abundance of C. waltoni was significantly highest in the 2014 warm season (June–October) when numerous minute (≤0.5 mm body length) juveniles were present. Vallicula multiformis was abundant in the 2015 and 2016 warm seasons (July–October), and also relatively abundant in the 2016 cool season (March–April). Ctenophore abundance and surface seawater temperature indicate a significant positive response to warm-season conditions in C. waltoni, whereas numbers of V. multiformis did not show any effect of seawater temperature. Recently settled individuals of V. multiformis (≤1.0 mm) occurred throughout the year. Individuals of V. multiformis recruited to fiber-coated sponges during warm and cool periods, with mean body sizes increasing in one cohort from 1.41 to 6.46 mm over a 39-d period, suggesting a growth rate of ~4% d−1. Feeding in both species involves tentacle capture of water-borne zooplankton and particulate organic matter. Individuals of C. waltoni were also observed inserting tentacles into octocoral polyps, possibly pilfering food. Chlorophyll a was detected in extracts of both ctenophore species. The high abundances and feeding behavior of benthic ctenophores could have a strong influence on octocoral and biofouling communities.

In this paper, we study the basic properties of last-in first-out (LIFO) preemptive-repeat single-server queues in which the server needs to start service from scratch whenever a preempted customer reaches the server. In particular, we study the question of when such queues are stable (in the sense that the equilibrium time-in-system is finite-valued with probability one) and show how moments of the equilibrium customer sojourn time can be computed when the system is stable. A complete analysis of stability is provided in the setting of Poisson arrivals and in that of the Markovian arrival process. The stability region depends upon the detailed structure of the interarrival and service time distributions and cannot be expressed purely in terms of expected values. This is connected to the fact that such preemptive-repeat queues are not work conserving.

Effective management of operating room resources relies on accurate predictions of surgical case durations. This prediction problem is known to be particularly difficult in pediatric hospitals due to the extreme variation in pediatric patient populations. We pursue two supervised learning approaches: (1) We directly predict the surgical case durations using features derived from electronic medical records and from hospital operational information. For this regression problem, we propose a novel metric for measuring accuracy of predictions which captures key issues relevant to hospital operations. We evaluate several prediction models; some are automated (they do not require input from surgeons) while others are semi-automated (they do require input from surgeons). We see that many of our automated methods generally outperform currently used algorithms and our semi-automated methods can outperform surgeons by a substantial margin. (2) We consider a classification problem in which each prediction provided by a surgeon is predicted to be correct, an overestimate, or an underestimate. This classification mechanism builds on the metric mentioned above and could potentially be useful for detecting human errors. Both supervised learning approaches give insights into the feature engineering process while creating the basis for decision support tools.

This paper studies a Geo/Geo/1$$+GI$$+GI queue in which the abandonments are endogenous. One crucial feature of this model is that the abandonment behavior is affected by the system performance and vice versa. Our model captures this interaction by developing two closely related models: an abandonment model and a queueing model. In the abandonment model, customers take the virtual waiting time distribution as given. They receive a reward r from service and incur a cost c per period of waiting. Customers are forward-looking and maximize their expected discounted utilities by making wait or abandon decisions dynamically as they wait in the queue. The queueing model takes the customers' abandonment time distribution as an input and studies the resulting virtual waiting time distribution. In equilibrium, the customers' abandonment behavior and the system performance must be consistent across the two models. Therefore, combining the two models and imposing this consistency requirement, we show that there exists a unique equilibrium. Lastly, we provide a computational scheme to calculate the equilibrium numerically.

This paper develops a general central limit theorem (CLT) for post-stratified Monte Carlo estimators with an associated infinite number of strata. In addition, consistency of the corresponding variance estimator is established in the same setting. With these results in hand, one can then construct asymptotically valid confidence interval procedures for such infinitely stratified estimators. We then illustrate our general theory, by applying it to the specific case of debiased multi-level Monte Carlo (MLMC) algorithms. This leads to the first asymptotically valid confidence interval procedure for such stratified debiased MLMC procedures.

Throughout the Galápagos, differences in coral reef development and coral population dynamics were evaluated by monitoring populations from 2000–2019, and environmental parameters (sea temperatures, pH, NO3−, PO43−) from 2015–19. The chief goal was to explain apparent coral community differences between the northern (Darwin and Wolf) and southern (Sta. Cruz, Fernandina, San Cristóbal, Española, Isabela) islands. Site coral species richness was highest at Darwin and Wolf. In the three most common coral taxa, a declining North (N)-South (S) trend in colony sizes existed for Porites lobata and Pocillopora spp., but not for Pavona spp. Frequent coral recruitment was observed in all areas. Algal competition was highest at Darwin, but competition by bioeroding sea urchins and burrowing fauna (polychaete worms, bivalve mollusks) increased from N to S with declining coral skeletal density. A biophysical model suggested strong connectivity among southern islands with weaker connectivity to Wolf and even less to Darwin. Also, strong connectivity was observed between Darwin and Wolf, but from there only intermittently to the south. From prevailing ocean current trajectories, coral larvae from Darwin and Wolf drift primarily towards Malpelo and Cocos Islands, some reaching Costa Rica and Colombia. Mean temperature, pH, and PO43− declined from N to S. Strong thermocline shoaling, especially in the warm season, was observed at most sites. A single environmental factor could not explain the variability in observed coral community characteristics, with minimum temperature, pH and nutrient levels the strongest determinants. Thus, complex environmental determinants combined with larval connectivity patterns may explain why the northern Galápagos Islands (Darwin, Wolf) have higher coral richness and cover and also recover more rapidly than central/southern islands after region-wide disturbances. These northern islands are therefore potentially of critical conservation importance as important reservoirs of regional coral biodiversity and source of larvae.