Solving Large‐Scale Weapon Target Assignment Problems in Seconds Using Branch‐Price‐And‐Cut

ABSTRACT This paper proposes a framework based on branch‐price‐and‐cut to solve the weapon target assignment (WTA) problem, a popular class of non‐linear assignment problems that has received significant attention over the past several decades. We first reformulate the WTA into a form amenable to column generation and then derive efficient algorithms for initializing the column generation, solving the pricing problem, generating clique cuts, and managing the branch‐and‐bound. Through significant experimentation, we display the framework's efficiency – which scales to solve problems with 10000 targets and weapons on a laptop and exactly solves problems in seconds, which previously took hours to solve. We also discuss extensions to common WTA variants and more general non‐linear assignment problems in hopes of motivating algorithmic developments.

Estimation of Minimal Clinically Important Difference for Tinnitus Handicap Inventory and Tinnitus Functional Index

Abstract Objective The minimal clinically important difference (MCID) represents the smallest change in treatment outcome deemed clinically meaningful. This study estimates the MCID for 2 widely used tinnitus measures: the Tinnitus Handicap Inventory (THI) and the Tinnitus Functional Index (TFI), using anchor‐based approaches while accounting for baseline severity and time interval. Study Design A multi‐center randomized clinical trial. Setting European tinnitus centers. Methods Anchor‐based approaches, including the effect size, receiver‐operating characteristics, and ΔTHI/TFI methods, were employed to determine the MCID. The “minimally improved” category of the Clinical Global Impression Scale‐Improvement (CGI‐I) served as the anchor. The standard error of measurement was used to assess random variation. Results For the THI, MCID estimates ranged from 7.8 to 12, with a point estimate of 11 after 12 weeks of treatment (N = 364). For the TFI, MCID estimates ranged from 7.3 to 9.4, with a point estimate of 9 points after 12 weeks (N = 359). Both measures indicated that higher baseline severity and longer time intervals required greater score reduction for clinical relevance. Conclusion This study highlights the context‐specific nature of MCID values for tinnitus measures and emphasizes the need for consensus on optimal anchor‐based approaches to improve comparability.

Paving the Way for Improved Representation of Coupled Physical and Biogeochemical Processes in Arctic River Plumes—A Case Study of the Mackenzie Shelf

ABSTRACT Processes affecting the transformation of riverine dissolved organic carbon (DOC) across the land‐to‐ocean aquatic continuum are still poorly constrained in Arctic models, leading to large uncertainties in simulated air–sea CO 2 fluxes of the coastal periphery. Here we use the ECCO‐Darwin regional configuration of the Southeastern Beaufort Sea to analyze the sensitivity of simulated carbon cycling to (1) the model vertical discretization and (2) different parameterizations of Mackenzie River carbon discharge. We show that riverine DOC lifetime rather than its volume largely modulates Mackenzie River plume air–sea CO 2 fluxes, leading to the Southeastern Beaufort Sea (SBS) being either a source (0.03 Tg C year −1 ) or sink (−0.20 Tg C year −1 ) of atmospheric carbon. We show that estuarine processes, such as flocculation, also play an important role and can dampen CO 2 outgassing by up to 0.07 Tg C year −1 . In terms of model physics, by increasing the vertical grid resolution, we better fit observed plume structure, without altering the simulated concentrations of DOC. However, the decrease in river forcing cell volume increases local p CO 2 and promotes elevated outgassing in the vicinity of the Delta. Our work demonstrates that future Arctic land–ocean models must consider the intricate details of river plume systems to realistically simulate coastal‐ocean physics and biogeochemistry.

A Joint Model for (Un)Bounded Longitudinal Markers, Competing Risks, and Recurrent Events Using Patient Registry Data

ABSTRACT Joint models for longitudinal and survival data have become a popular framework for studying the association between repeatedly measured biomarkers and clinical events. Nevertheless, addressing complex survival data structures, especially handling both recurrent and competing event times within a single model, remains a challenge. This causes important information to be disregarded. Moreover, existing frameworks rely on a Gaussian distribution for continuous markers, which may be unsuitable for bounded biomarkers, resulting in biased estimates of associations. To address these limitations, we propose a Bayesian shared‐parameter joint model that simultaneously accommodates multiple (possibly bounded) longitudinal markers, a recurrent event process, and competing risks. We use the beta distribution to model responses bounded within any interval( a , b ) $$ \left(a,b\right) $$ without sacrificing the interpretability of the association. The model offers various forms of association, discontinuous risk intervals, and both gap and calendar timescales. A simulation study shows that it outperforms simpler joint models. We utilize the US Cystic Fibrosis Foundation Patient Registry to study the associations between changes in lung function and body mass index, and the risk of recurrent pulmonary exacerbations, while accounting for the competing risks of death and lung transplantation. Our efficient implementation allows fast fitting of the model despite its complexity and the large sample size from this patient registry. Our comprehensive approach provides new insights into cystic fibrosis disease progression by quantifying the relationship between the most important clinical markers and events more precisely than has been possible before. The model implementation is available in the R package JMbayes2 .

Risk‐Profile Based Monitoring Intervals for Multivariate Longitudinal Biomarker Measurements and Competing Events With Applications in Stable Heart Failure

ABSTRACT Patient monitoring is routinely used to detect disease aggravation in many chronic conditions. We propose an adaptive scheduling strategy based on dynamic individual risk predictions that can improve the efficiency of monitoring programs that incorporate multiple longitudinal measurements and competing events. It is motivated by stable chronic heart failure (CHF) patients who are periodically seen to assess the risk of disease aggravation based on multiple patient characteristics and circulating marker protein levels such as NT‐proBNP and troponin. We assess the performance of the adaptive strategy versus fixed schedule alternatives using a simulation study based on the Bio‐SHiFT study, a cohort of stable CHF patients.

Personalized Biopsy Schedules Using an Interval‐Censored Cause‐Specific Joint Model

ABSTRACT Active surveillance (AS), where biopsies are conducted to detect cancer progression, has been acknowledged as an efficient way to reduce the overtreatment of prostate cancer. Most AS cohorts use fixed biopsy schedules for all patients. However, the ideal test frequency remains unknown, and the routine use of such invasive tests burdens the patients. An emerging idea is to generate personalized biopsy schedules based on each patient's progression‐specific risk. To achieve that, we propose the interval‐censored cause‐specific joint model (ICJM), which models the impact of longitudinal biomarkers on cancer progression while considering the competing event of early treatment initiation. The underlying likelihood function incorporates the interval‐censoring of cancer progression, the competing risk of treatment, and the uncertainty about whether cancer progression occurred since the last biopsy in patients that are right‐censored or experience the competing event. The model can produce patient‐specific risk profiles up to a horizon time. If the risk exceeds a certain threshold, a biopsy is conducted. The optimal threshold can be chosen by balancing two indicators of the biopsy schedules: The expected number of biopsies and the expected delay in detection of cancer progression. A simulation study showed that our personalized schedules could considerably reduce the number of biopsies per patient by 41%–52% compared to the fixed schedules, though at the cost of a slightly longer detection delay.

Precision of Treatment Hierarchy: A Metric for Quantifying Certainty in Treatment Hierarchies From Network Meta‐Analysis

ABSTRACT Network meta‐analysis (NMA) is an extension of pairwise meta‐analysis that facilitates the estimation of relative effects for multiple competing treatments. A hierarchy of treatments is a useful output of an NMA. Treatment hierarchies are produced using ranking metrics. Common ranking metrics include the Surface Under the Cumulative RAnking curve (SUCRA) and P‐scores, which are the frequentist analogue to SUCRAs. Both metrics consider the size and uncertainty of the estimated treatment effects, with larger values indicating a more preferred treatment. Although SUCRAs and P‐scores themselves consider uncertainty, treatment hierarchies produced by these ranking metrics are typically reported without a measure of certainty, which might be misleading to practitioners. We propose a new metric, Precision of Treatment Hierarchy (POTH), which quantifies the certainty in producing a treatment hierarchy from SUCRAs or P‐scores. The metric connects three statistical quantities: The variance of the SUCRA values, the variance of the mean rank of each treatment, and the average variance of the distribution of individual ranks for each treatment. POTH provides a single, interpretable value that quantifies the extent of certainty in producing a treatment hierarchy. We show how the metric can be adapted to apply to subsets of treatments in a network, for example, to quantify the certainty in the hierarchy of the top three treatments. We calculate POTH for a database of NMAs to investigate its empirical properties, and we demonstrate its use on two published networks.

Efficient Microwave‐Assisted Glycolysis of PET Fiber Waste: Optimization, Products Analysis, and BHET Recovery for Sustainable Recycling

Abstract In this study, the glycolysis of poly(ethylene terephthalate) (PET) fiber waste was conducted using excess ethylene glycol (EG) as the glycolysis agent and manganese acetate (Mn(Ac) 2 ) as the catalyst under microwave irradiation. Various parameters, such as reaction time, reaction temperature, PET:EG molar ratio, and the weight ratio of Mn(Ac) 2 to PET, were studied to enhance the degree of PET conversion. The experimental results showed that the reaction time was significantly decreased under microwave irradiation as compared with conventional heating. The purpose of this study was to identify the oligomers of PET as glycolysis products and to separate them, with the main goal being the isolation of monomer bis(hydroxyethyl terephthalate) (BHET). For this reason, the glycolysis products were analyzed and identified by Fourier transform infrared spectroscopy (FTIR) and Differential scanning calorimetry (DSC). Finally, at the optimal glycolysis temperature, the yield of BHET was calculated as a function of time. BHET isolation is particularly important for industries reliant on PET, such as packaging, textiles, and beverage containers, where recycled PET can be reintroduced as a sustainable alternative, promoting the principles of a circular economy and mitigating the environmental impact.

Report from the WPA Section on Psychiatry and Sleep‐Wakefulness Disorders

Public capital investment spike events: the regional dimension in Greece