Assignment Rules Research Articles

Post‐Acute Care and ACOs — Who Will Be Accountable?

To determine how the inclusion of post-acute evaluation and management (E&M) services as primary care affects assignment of Medicare beneficiaries to accountable care organizations (ACOs). Medicare claims for a random 5 percent sample of 2009 Medicare beneficiaries linked to American Medical Association Group Practice data identifying provider groups sufficiently large to be eligible for ACO program participation. We calculated the fraction of community-dwelling beneficiaries whose assignment shifted, as a consequence of including post-acute E&M services, from the group providing their outpatient primary care to a different group providing their inpatient post-acute care. Assignment shifts occurred for 27.6 percent of 25,992 community-dwelling beneficiaries with at least one post-acute skilled nursing facility stay, and they were more common for those incurring higher Medicare spending. Those whose assignment shifted constituted only 1.3 percent of all community-dwelling beneficiaries cared for by large ACO-eligible organizations (n=535,138), but they accounted for 8.4 percent of total Medicare spending for this population. Under current Medicare assignment rules, ACOs may not be accountable for an influential group of post-acute patients, suggesting missed opportunities to improve care coordination and reduce inappropriate readmissions.

A method for estimating population sex ratio for sage‐grouse using noninvasive genetic samples

Population sex ratio is an important metric for wildlife management and conservation, but estimates can be difficult to obtain, particularly for sexually monomorphic species or for species that differ in detection probability between the sexes. Noninvasive genetic sampling (NGS) using polymerase chain reaction (PCR) has become a common method for identifying sex from sources such as hair, feathers or faeces, and is a potential source for estimating sex ratio. If, however, PCR success is sex-biased, naively using NGS could lead to a biased sex ratio estimator. We measured PCR success rates and error rates for amplifying the W and Z chromosomes from greater sage-grouse (Centrocercus urophasianus) faecal samples, examined how success and error rates for sex identification changed in response to faecal sample exposure time, and used simulation models to evaluate precision and bias of three sex assignment criteria for estimating population sex ratio with variable sample sizes and levels of PCR replication. We found PCR success rates were higher for females than males and that choice of sex assignment criteria influenced the bias and precision of corresponding sex ratio estimates. Our simulations demonstrate the importance of considering the interplay between the sex bias of PCR success, number of genotyping replicates, sample size, true population sex ratio and accuracy of assignment rules for designing future studies. Our results suggest that using faecal DNA for estimating the sex ratio of sage-grouse populations has great potential and, with minor adaptations and similar marker evaluations, should be applicable to numerous species.

Notes de recheche sur l'accentuation et le phrasé prosodique à la lumière des corpus de français

In this paper the main rules for stress assignment in French, the constraints relative to the formation of minor prosodic units (Accentual Phrases) and major prosodic units (Intonational Phrases, IP) are first recalled. A procedure developed in order to annotate semi-automatically these phenomena in different French corpora is then described. Finally the results of two studies are presented. The first one is conducted on a 4-hour long corpus (designed for the statistical study of regional variation in Swiss French). It aims at testing the robustness of two phonological rules implicated in the formation of Accentual Phrase in French (deaccentuation of pre-nominal adjectives (a sequel to the Align-XHead constraint), accentual clash avoidance rule). The second summarizes the main results obtained in the author’s PhD thesis (Avanzi, 2012). It is conducted on a set of data containing approximately 800 dislocated-sentences. It questions the weight of syntactic and information structure cues in front of prosodic structure cues in order to identify Intonational Phrase boundaries.

Inference on Optimal Treatment Assignments

We consider inference on optimal treatment assignments. Our methods allow for inference on the treatment assignment rule that would be optimal given knowledge of the population treatment effect in a general setting. The procedure uses multiple hypothesis testing methods to determine a subset of the population for which assignment to treatment can be determined to be optimal after conditioning on all available information, with a prespecified level of confidence. A Monte Carlo study confirms that the inference procedure has good small sample behavior. We apply the method to study Project STAR and the optimal assignment of small class based on school and teacher characteristics.

Cyclic Scheduling of Sequentially Connected Cluster Tools with a Single I/O Module

A cluster tool has been widely used for most wafer or flat panel fabrication processes. We examine cyclic scheduling problems of sequentially connected cluster tools with a single input and output module, which includes multi-cluster tools as well as linear cluster tools. We first compute workloads for each process step and each robot, of which the maximum provide a lower bound of the tool cycle time. We then identify an assignment rule of assigning the chambers to the process steps that makes the tool cycle time independent of the order of using the parallel chambers. We also propose a robot task sequence for each component cluster tool, which is modified from the well-known swap sequence. We prove that the modified swap sequence is optimal when one of the process steps, not a robot, is the bottleneck. Finally, we present a scheduling strategy which controls robot task timings to deal with interference of wafer flows between each pair of adjacent component cluster tools, which is complicated by blocking due to limited buffer capacity between them and backward wafer flows.

A hybrid genetic approach for multi-objective and multi-platform large volume surveillance problem

Efficient management of surveillance assets and successful scheduling of surveillance tasks are complex decision-making problems for the execution of large volume surveillance missions in order to improve security and safety. A mission can be seen as a defined set of logical ordered tasks with time and space constraints. The resources to task assignment rules require that available assets should be allocated to each task. A combination of assets might be required to execute a given task. Finding efficient management solutions should be investigated to optimise assets-resources allocation and tasks scheduling. In this paper, we propose to model this optimisation problem as a multi-objective, multi-platform assignment and scheduling problem. Resources are to be assigned to accomplish different tasks. Surveillance tasks should be scheduled into successive periods. The problem is designed to consider two conflicting objective functions: minimising the makespan and minimising the total cost. As the problem is NP-hard, a hybrid genetic algorithm HGA is proposed. The empirical validation is performed using a simulation environment called Inform Lab, and a comparison to two state-of-the-art multi-objective approaches based on selected performance metrics. The experimental results show that HGA performs consistently well for high dimensional problems.

Mental Health Issues of Women Deployed to Iraq and Afghanistan

The number of women serving in the military and deployed to active-duty is unprecedented in the history of the United States. When women became a permanent sector of the U.S. Armed Services in 1948, their involvement was restricted to comprise only 2% of the military population; today women constitute approximately 14.5% of the 1.4 million active component and 18% of the 850,000 reserve component. Yet, little attention has been paid to the mental health needs of women military members. This review article highlights the history of women in the military and then focuses on the impact of combat exposure and injuries, military sexual trauma, alcohol use, and family separations which are associated with PTSD, depression, suicide, difficulty with reintegration, and homelessness.

On estimating average effects for multiple treatment groups

We propose to estimate average exposure (or treatment) effects from observational data for multiple exposure groups by fitting an approximation of the marginal sample distribution of the response variable in each exposure group to the data. The marginal sample distribution is a function of the true distribution of the response variable in the population and the assignment rule governing the allocation of the subjects to different exposure groups. The assignment rule can depend on the response variable in addition to measured covariates, and hence the method is appropriate even when the assumption of ignorable treatment assignment is not justified. We estimate the exposure effects are estimated based on the population expectation (PE) of the outcome variable. We compare the PE approach with an instrumental variable method and with several other methods including propensity score based approaches that assume ignorable assignment mechanisms. We evaluate the robustness of the PE method under model misspecifications and illustrate it using data from a study of the impact of soy consumption on urinary concentrations of estrogen and estrogen metabolites in Asian American women. Published 2012. This article is a US Government work and is in the public domain in the USA.

Performance analysis and design trade-offs in warehouses with autonomous vehicle technology

Distribution centers have recently adopted Autonomous Vehicle-based Storage and Retrieval Systems (AVS/RSs) as a potential alternative to traditional automated storage and retrieval systems for processing unit-load operations. The autonomy of the vehicles in an AVS/RS provides a level of hardware sophistication, which can lead to the improvements in operation efficiency and flexibility that will be necessary in distribution centers of the future. However, in order to exploit the potential benefits of the technology, an AVS/RS must be designed using a detailed understanding of the underlying dynamics and performance trade-offs. Design decisions such as the configuration of aisles and columns, allocation of resources to zones, and vehicle assignment rules can have a significant impact on the performance of AVS/RSs. In this research, the performance impact of these design decisions is investigated using an analytical model. The system is modeled as a multi-class semi-open queuing network with class switching and a decomposition-based approach is developed to evaluate the system performance and obtain insights. Numerical studies provide various insights that could be useful in the design conceptualization of AVS/RSs.

Allen's defence of the Extended OrderingHypothesis: a critical appraisal

(1) ORDERING HYPOTHESIS A. In English, Class I affixation precedes Class II affixation. B. The cyclic stress assignment rules follow Class I affixation and precede Class II affixation.

Generating a pronunciation dictionary for European Portuguese using a joint-sequence model with embedded stress assignment

AbstractThis paper addresses the problem of grapheme to phoneme conversion to create a pronunciation dictionary from a vocabulary of the most frequent words in European Portuguese. A system based on a mixed approach funded on a stochastic model with embedded rules for stressed vowel assignment is described. The implemented model can generate pronunciations from unrestricted words; however, a dictionary with the 40k most frequent words was constructed and corrected interactively. The dictionary includes homographs with multiplepronunciations. The vocabulary was defined using the CETEMPúblico corpus. The model and dictionary are publicly available.

Does the quality of public-sponsored training programs matter? Evidence from bidding processes data

This paper analyzes the link between training quality and labor-market outcomes. Multiple proxies for training quality are identified from bidding processes in which public and private training institutions compete for limited public funding in Peru. Information about exact dates of program enrollment is analyzed to show whether the first-come-first-served assignment rule randomized eligible individuals across courses of varying quality. Generalized propensity score (GPS) is implemented to estimate dose–response functions in the context of multiple treatments. We find that beneficiaries attending high-quality training courses show higher earnings and better job-quality characteristics than either beneficiaries attending low-quality courses or nonparticipants. The returns are particularly robust for women, making the provision of high-quality training services cost-effective. Furthermore, the most important training attribute is expenditures per trainee. Class size and infrastructure are weakly related to the expected impacts, while teacher experience, curricular activities, and market knowledge seem to bear no relationship with the expected impacts. External validity was assessed by using five cohorts of individuals over an eight-year period.

Clinical Utility in Evaluation of Risk Models

Statistical and clinical perspectives on risk models can be very different, even with agreement on the objective: to develop accurate and precise risk estimates for rational, effective, and cost-effective prevention strategies (1). For example, Katki et al. (2) estimated the risk of cervical precancer (cervical intraepithelial neoplasia grade 3) or worse conditional on results of concurrent testing for human papillomavirus (HPV) and cervical cytology (cotesting) in 300,000 women 30 years of age or older who were enrolled in a large health maintenance organization. The nearly equivalent risk over 5 years for all the women who were HPV negative (3.8 per 100,000 per year) and those who were both HPV negative and cytology negative (3.2 per 100,000 per year) might justify a prevention program using primary screening based on results of HPV testing alone. In contrast, among 17,000 HPV-positive women, the 5-year risk of cervical intraepithelial neoplasia grade 3 or worse was substantially lower (5.9%) in the 12,000 women with normal cytology than it was in the 5,000 women who had abnormal cytology (12.1%). This suggests that cytology testing might improve decisions about management of women who test positive for HPV. As exemplified by Katki et al. (2), the value of an additional clinical test is a function of simple risks or generalizations of predictive values, along with proportions with the different clinical presentations. Both a risk model and a clinical test are risk classifiers or risk stratifiers. For evaluation of risk models, as Cook (3) notes in one commentary discussing an article by Pencina et al. (4) in this issue of the Journal, neither area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), nor net reclassification improvement (NRI) is necessary when direct evaluation of clinical performance of a program based on the risk model is possible. The measure of clinical utility is difference in rates or risk of clinical outcomes from intervention strategies using rules for assignment based on the different models. The differences in risk and the distribution of the possible clinical presentation from the models are measures of risk stratification or of the extra precision resulting from the added complexity of a model. Of course, clinical utility also depends on the efficacy of available interventions; a model with prefect predictions has no clinical utility without an effective intervention. Pencina et al. (4) discussed measures for evaluating risk models by purely statistical assessment without consideration of interventions with known efficacy, which is needed for evaluating clinical utility. They advocated reporting IDI and NRI in addition to the most commonly used measure, AUC. AUC is a nonparametric test statistic (the Mann–Whitney U test equivalent to a Wilcoxon rank sum test) of equality of distribution of estimated risk in cases and controls. The IDI adds to the AUC because an estimate of difference in means adds information not available from a statistic that is based on ranks and not actual values. NRIs can capture effects of differences in distribution, including spread and skewness, that are not reflected in the difference in means. As noted by Pencina et al. (4), 2 of the American Heart Association's 2009 criteria for evaluation of novel markers of cardiovascular risk are “documentation of incremental information when added to standard risk markers” and “assessment of effects on patient management and outcomes” (5, p. 2408). As noted by Kerr et al. (6), assessment of the marginal increase in utility from an improved model requires an objective assessment of effects on patient management and outcomes; the incremental information criterion needs to be a purely statistical measure if it is separate from clinical utility. The difference between the clinical and statistical views is manifested in one particularly important way: the role of variability in the risk. Less variability, or more homogeneity, in risk within cases and controls increases the AUC and other measures of discrimination; in contrast, greater variation in risk in the study population for which decisions are to be made increases the potential for assigning a intervention for those at extreme risks different from the intervention appropriate for one at average risk. In other words, small variance in risk, conditional on disease, increases discrimination, but large unconditional variance increases the potential for clinical utility. Of course, the variation needs to be real, not a consequence of random variation of risk estimates or misclassification of markers in the model. This exchange of views (1, 2, 6, 7) highlights the differences between the clinical and statistical perspectives on risk models. The connection between the 2 perspectives is not yet clear to all.

Rule-Based Scheduling of Single-Stage Multiproduct Batch Plants with Parallel Units

A new rule-based model for single-stage multiproduct scheduling problem (SMSP) in batch plants with parallel units is proposed. The scheduling problem is decomposed into two subproblems of order assignment and order sequencing. A group of order assignment rules are introduced to select suitable units for customer orders. While flexible constraint handling strategies are proposed to eliminate the constraints of forbidden units and unit release times and convert the hard constraint of forbidden sequences to a soft one, leading to any arbitrary order sequence can be synthesized into a legal schedule. The line-up competition algorithm (LUCA) is presented to obtain optimal order sequence and order assignment rule, which can minimize makespan, total tardiness, or total cost. Through comparative study, the proposed approach has demonstrated its effectiveness for solving a large size SMSP.

An Agent-Based Solution Framework for Inter-Block Yard Crane Scheduling Problems

The efficiency of yard operations is critical to the overall productivity of a container terminal because the yard serves as the interface between the landside and waterside operations. Most container terminals use yard cranes to transfer containers between the yard and trucks (both external and internal). To facilitate vessel operations, an efficient work schedule for the yard cranes is necessary given varying work volumes among yard blocks with different planning periods. This paper investigated an agent-based approach to assign and relocate yard cranes among yard blocks based on the forecasted work volumes. The goal of our study is to reduce the work volume that remains incomplete at the end of a planning period. We offered several preference functions for yard cranes and blocks which are modeled as agents. These preference functions are designed to find effective schedules for yard cranes. In addition, we examined various rules for the initial assignment of yard cranes to blocks. Our analysis demonstrated that our model can effectively and efficiently reduce the percentage of incomplete work volume for any real-world sized problem.

Cost Optimization Problem of Hybrid Flow-Shop Based on PSO Algorithm

A PSO-algorithm-based job scheduling method that takes production cost as optimization object is presented in this paper. The cost optimization model of HFSP, in which production cost is considered as an optimal factor, is constructed. PSO is used to take global optimization, make the production task assignment and find which machine the jobs should be assigned at each stage, which is also called the process route of the job. After that the local assignment rules are used to determine the job’s starting time and processing sequence at each stage. The total production cost converted by time-based scheduling results is comprehensively considering the processing cost, waiting costs, and the products storage costs. The numerical results show the effectiveness of the algorithm after comparing between multi-group programs.

Evaluating the Effects of University Grants by Using Regression Discontinuity Designs

SummaryThe paper evaluates the effects of Italian university grants on student dropout. Eligible applicants receive a grant if their family economic indicator is below a specified threshold, so the grant assignment rule appeals to a regression discontinuity design. After a brief introduction to regression discontinuity designs, the particular setting that is considered in the paper is formalized. Difference-in-difference type assumptions are introduced to identify and estimate the effect away from the threshold. Empirical results show that, at the threshold, the grant is an effective tool to prevent students from low income families from dropping out of higher education. However, there is some evidence that the effect of the grant becomes smaller and not significant for poorer students who are further from the threshold.

Species Within the Bemisia tabaci (Hemiptera: Aleyrodidae) Complex in Soybean and Bean Crops in Argentina

The whitefly Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is a cryptic species complex that contains some of the most damaging pests in tropical and subtropical regions. Recent studies suggested that this complex is composed of at least 24 distinct species. We use the approach from these studies to consider the identity of B. tabaci in Argentina. Previous studies have suggested the presence of a B. tabaci presumably indigenous to the Americas and referred to as the BR biotype in Argentina. We placed the entity referred to as the BR biotype within the B. tabaci cryptic species complex using whiteflies collected in soybean and bean crops in northern and central Argentina. The whiteflies were assigned using the mitochondrial cytochrome oxidase (mtCOI) gene. Four unknown haplotypes plus two Argentina sequences from GenBank formed a cluster that was basal to the rest of the New World sequences. These sequences diverged from the consensus sequence across the range of 3.6 to 4.3%. Applying the species assignment rules of recent studies suggests that the individuals from Argentina form a separate species. A fifth unknown haplotype fell within the New World putative species and formed a distinct cluster with haplotypes from Panama. These results suggest that Argentina has two indigenous species belonging to the B. tabaci cryptic species complex. Rather than using mtCOI sequencing for all B. tabaci collected, a simple random amplified polymorphic DNA-polymerase chain reaction diagnostic was used and tested along with previously published primers designed to work specifically with the BR biotype from Brazil. These primers were either unable to distinguish between the two indigenous members of the complex in Argentina or indicated a difference when none was evident on the basis of mtCOI sequence comparison.

Cost Optimization Problem of Hybrid Flow-Shop Based on Differential Evolution Algorithm

A differential evolution algorithm based job scheduling method is presented, whose optimization target is production cost. The cost optimization model of hybrid flow-shop is thereby constructed through considering production cost as a factor in scheduling problem of hybrid flow-shop. In the implementation process of the method, DE is used to take global optimization and find which machine the jobs should be assigned on at each stage, which is also called the process route of the job; then the local assignment rules are used to determine the job’s starting time and processing sequence at each stage. With converting time-based scheduling results to fitness function which is comprehensively considering the processing cost, waiting costs, and the products storage costs, the processing cost is taken as the optimization objective. The numerical results show the effectiveness of the algorithm after comparing between multi-group programs.

Groupwise information sharing promotes ingroup favoritism in indirect reciprocity.

BackgroundIndirect reciprocity is a mechanism for cooperation in social dilemma situations. In indirect reciprocity, an individual is motivated to help another to acquire a good reputation and receive help from others afterwards. Another aspect of human cooperation is ingroup favoritism, whereby individuals help members in their own group more often than those in other groups. Ingroup favoritism is a puzzle for the theory of cooperation because it is not easily evolutionarily stable. In the context of indirect reciprocity, ingroup favoritism has been shown to be a consequence of employing a double standard when assigning reputations to ingroup and outgroup members. An example of such a double standard is the situation in which helping an ingroup member is regarded as good, whereas the same action toward an outgroup member is regarded as bad.ResultsWe analyze a computational model of indirect reciprocity in which information sharing is conducted groupwise. In our model, individuals play social dilemma games within and across groups, and the information about their reputations is shared within each group. We show that evolutionarily stable ingroup favoritism emerges even if all the players use the same reputation assignment rule regardless of group (i.e., a single standard). Two reputation assignment rules called simple standing and stern judging yield ingroup favoritism; under these rules, cooperation with (defection against) good individuals is regarded as good (bad) and defection against bad individuals is regarded as good. Stern judging induces much stronger ingroup favoritism than does simple standing. Simple standing and stern judging are evolutionarily stable against each other when groups employing different assignment rules compete and the number of groups is sufficiently large. In addition, we analytically show as a limiting case that homogeneous populations of reciprocators that use reputations are unstable when individuals independently infer reputations of individuals, which is consistent with previously reported numerical results.ConclusionsOur results suggest that ingroup favoritism can be promoted in indirect reciprocity by the groupwise information sharing, in particular under the stern judging assignment rule.