Initial Strategy Research Articles

Rapid start-up and operational characteristics of partial denitrification coupled with anammox driven by innovative strategies

The Partial Denitrification-Anammox (PD/A) process established a low-consumption, efficient and sustainable pathway for complete nitrogen removal, which is of great interest to the industry. Rapid initiation and stable operation of the PD/A systems were the main issues limiting its engineering application in wastewater nitrogen removal. A PD/A system was initiated in a continuous stirred-tank reactors (CSTRs) in the presence of low concentration of organic matter, and the effects of organic matter types and COD/NO3−-N ratios on the performance of the PD/A system, and microbial community characteristics were explored. The results showed that low concentrations of organic matter could promote the rapid initiation of the Anammox process and then the strategy of gradually replacing NO2−-N with NO3−-N could successfully initiate the PD/A system at 70 days. The type of organic matter had a significant effect on the initiation of the Anammox and the establishment of the PD/A system. Compared to glucose, sodium acetate was more favorable for rapid start-up and the synergy among microorganisms, and organic matter was lower, with an optimal COD/NO3−-N ratio of 3.0. Microorganisms differed in their sensitivity to environmental factors. The relative abundance of Planctomycetota and Proteobacteria in R2 was 51 %, with the presence of three typical anammox bacteria, Candidatus_Brocadia, Candidatus_Kuenenia, and Candidatus_Jettenia in the system. This study provides a new strategy for the rapid initiation and stable operation of the PD/A process.

Strategic postponement of coalition policymaking in European Parliamentary democracies

AbstractCoalition policymaking concerns not only who decides what in which jurisdiction but also when, how speedy and in what rhythm. Due to the limited time budget and shadow of future elections, parties in charge of respective ministerial portfolios have to strategically organize their policy agendas to trade off between policy and electoral incentives in the face of coalition partners who monitor and control ministerial autonomy. However, despite the burgeoning literature on coalition governance, the temporal dimension of ministerial agenda control is less well understood. I advance this research by proposing a model to directly account for the influence of time budgets on timing decisions of ministers in policy initiation. In this model, I distinguish between different timing strategies of policy initiation a ministerial party may possibly adopt and identify in equilibrium a conditional postponing strategy by which ministers facing high scrutiny of coalition partners will postpone bill initiation till the end of the term. The empirical examination lends support to my argument and further demonstrates that the timing strategy of ministers can also be influenced by coalition conflict and policy saliency of bills.

Telephone-Based Guideline-Directed Medical Therapy Optimization in Navajo Nation

Underutilization of guideline-directed medical therapy for heart failure with reduced ejection fraction is a major cause of poor outcomes. For many American Indian patients receiving care through the Indian Health Service, access to care, especially cardiology care, is limited, contributing to poor uptake of recommended therapy. To examine whether a telehealth model in which guideline-directed medical therapy is initiated and titrated over the phone with remote telemonitoring using a home blood pressure cuff improves guideline-directed medical therapy use (eg, drug classes and dosage) in patients with heart failure with reduced ejection fraction in Navajo Nation. The Heart Failure Optimization at Home to Improve Outcomes (Hózhó) randomized clinical trial was a stepped-wedge, pragmatic comparative effectiveness trial conducted from February to August 2023. Patients 18 years and older with a diagnosis of heart failure with reduced ejection fraction receiving care at 2 Indian Health Service facilities in rural Navajo Nation (defined as having primary care physician with 1 clinical visit and 1 prescription filled in the last 12 months) were enrolled. Patients were randomized to the telehealth care model or usual care in a stepped-wedge fashion, with 5 time points (30-day intervals) until all patients crossed over into the intervention. Data analyses were completed in January 2024. A phone-based telehealth model in which guideline-directed medical therapy is initiated and titrated at home, using remote telemonitoring with a home blood pressure cuff. The primary outcome was an increase in the number of guideline-directed classes of drugs filled from the pharmacy at 30 days postrandomization. Of 103 enrolled American Indian patients, 42 (40.8%) were female, and the median (IQR) age was 65 (53-77) years. The median (IQR) left ventricular ejection fraction was 32% (24%-36%). The primary outcome occurred significantly more in the intervention group (66.2% vs 13.1%), thus increasing uptake of guideline-directed classes of drugs by 53% (odds ratio, 12.99; 95% CI, 6.87-24.53; P < .001). The number of patients needed to receive the telehealth intervention to result in an increase of guideline-directed drug classes was 1.88. In this heart failure trial in Navajo Nation, a telephone-based strategy of remote initiation and titration for outpatients with heart failure with reduced ejection fraction led to improved rates of guideline-directed medical therapy at 30 days compared with usual care. This low-cost strategy could be expanded to other rural settings where access to care is limited. ClinicalTrials.gov Identifier: NCT05792085.

An adjustment method of shared private idle parking spaces matching scheme considering demanders cancel reservations

An adjustment method for the initial matching scheme between private idle parking spaces and demanders is proposed in this paper. We consider a shared parking platform to determine the initial matching scheme. However, the demanders may cancel reservations due to the change of destinations or parking time before the matching scheme is executed. Meanwhile, new demanders make reservations. We develop a multi-objective optimization model satisfies adjustment principles to obtain the adjusted matching scheme that mitigates the negative impacts of cancellation reservation. The objective is to maximize the satisfaction of demanders, the satisfaction of private idle parking spaces, and the platform profit. The improved nondominated sorting genetic algorithm II (INSGA-II) is then developed to solve the model and determine the adjusted matching scheme. Our results suggest that the performance of the adjusted matching scheme outperforms that of non-adjusted matching scheme for various experimental sizes.

Learnable weight initialization for volumetric medical image segmentation

Hybrid volumetric medical image segmentation models, combining the advantages of local convolution and global attention, have recently received considerable attention. While mainly focusing on architectural modifications, most existing hybrid approaches still use conventional data-independent weight initialization schemes which restrict their performance due to ignoring the inherent volumetric nature of the medical data. To address this issue, we propose a learnable weight initialization approach that utilizes the available medical training data to effectively learn the contextual and structural cues via the proposed self-supervised objectives. Our approach is easy to integrate into any hybrid model and requires no external training data. Experiments on multi-organ and lung cancer segmentation tasks demonstrate the effectiveness of our approach, leading to state-of-the-art segmentation performance. Our proposed data-dependent initialization approach performs favorably as compared to the Swin-UNETR model pretrained using large-scale datasets on multi-organ segmentation task. Our source code and models are available at: https://github.com/ShahinaKK/LWI-VMS.

Design and optimization of liquid-cooled plate structure for power battery of the pure electric excavator

With the increasing pressure of energy transformation and environmental protection, the trend of electrification of construction machinery is becoming more and more obvious. In this paper, based on a small pure electric excavator which is still in the stages of research and development, a liquid-cooled heat dissipation structure (liquid-cooled plate) is designed according to the power battery pack scheme. The overall shape of the liquid-cooled plate is designed as a symmetrical serpentine flow channel. Geometrically, the symmetrical serpentine flow channel is a combination of a straight flow channel and a runway-shaped flow channel. Then the conjugate heat transfer simulation model was established by using the CFD software Ansys Fluent. The results show that the initial scheme can not guarantee the temperature homogeneity of the battery pack. Therefore, the liquid-cooled heat dissipation scheme was optimized and compared. Firstly, a liquid-cooled plate was added on the original basis, and multiple sets of simulations were carried out with the length of the linear flow channel as a variable. The results show that appropriately increasing the length of the straight flow channel at the entrance of the liquid-cooled plate has a marked effect on improving the heat dissipation performance.

Differentially Private Functional Encryption

We address the question of realizing privacy preserving analysis of user data. The abstract scenario considered is that an analyst aims to evaluate a function f on some user data X. To achieve comprehensive privacy, it is necessary to protect the input X directly. However, it is known that f(X) may leak too much information about X as well. A common approach to mitigate such risks is to make the computation differential private. In practice, this is often accomplished by replacing f by a noisy variant f^*. We investigate the use of multi-input functional encryption (MIFE) for achieving input- and output-privacy in one cryptographic mechanism. In a MIFE scheme, a setup authority can generate restricted decryption keys which enable to learn specific functions of encrypted messages, without revealing any additional information. To achieve differential privacy in this process, we introduce as a new cryptographic primitive: noisy multi-input functional encryption (NMIFE). It extends the concept of MIFE such that the decryption key may also encode a noisy function where the noise value is secret. While the change from MIFE to NMIFE is rather straightforward, the challenge is to come up with precise and workable definitions of correctness and security definition that we propose and explain in this work. Here, the security definition is tailored to the use case of differential privacy. As it is a special case of the established notion of full-hiding security, we present a generic transformation that allows to turn any full-hiding MIFE scheme into a secure NMIFE scheme that has practically the same performance as the initial MIFE scheme. Moreover, we make use of the fact that the proposed security definition is less restrictive and present a new concrete NMIFE scheme for evaluating the inner product. It is dubbed DiffPIPE (short for DIFFerentially Private Inner Product Evaluation). DiffPIPE is not the result from the transformation and outperforms all from existing full-hiding MIFE schemes constructed NMIFE schemes. In experiments, we demonstrate its applicability for realizing privacy preserving counting queries on data sets.

Rediversion of the Failing Ileoanal Pouch: First Step in Pouch Salvage?

Ileal pouch-anal anastomosis is a technically demanding procedure with many potential complications. Rediversion with an ileostomy is often the first step in pouch salvage; however, it may not be clear if an individual patient will undergo subsequent pouch salvage surgery. We aimed to describe the indications and short- and long-term outcomes of rediversion in our pouch registry. We queried our institutional pouch registry for patients who underwent index 2- or 3-stage IPAA and subsequent rediversion at our institution between 1985 and 2022. Pouches constructed elsewhere, rediverted elsewhere, or those patients who underwent pouch salvage/excision without prior rediversion were excluded. Patients were selected for pouch salvage according to the surgeon's discretion. Overall, 177 patients (3.4% of 5207 index pouches) were rediverted. At index pouch, median patient age was 32 years and 50.8% were women. Diagnoses included ulcerative colitis (86.4%), indeterminate colitis (6.2%), familial adenomatous polyposis (4.0%), and others (3.4%). Median time from prior ileostomy closure to rediversion was 7.2 years. Indications for rediversion were inflammatory in 98 (55.4%) and noninflammatory in 79 (44.6%) patients. After rediversion, 52% underwent pouch salvage, 30% had no further surgery, and 18.1% underwent pouch excision. The 5-year pouch survival rates for inflammatory and noninflammatory indications were 71.5% and 94.5%, respectively (P = .02). Rediversion of ileoanal pouches is a safe initial strategy to manage failing pouches and is a useful first step in pouch salvage in many patients. Subsequent salvage surgery for noninflammatory indications had a significantly higher pouch salvage rate than those rediverted for inflammatory complications.

Intercomparison of initialization methods for seasonal-to-decadal climate predictions with the NorCPM

Initialization is essential for accurate seasonal-to-decadal (S2D) climate predictions. The initialization schemes used differ on the component initialized, the Data Assimilation method, or the technique. We compare five popular schemes within NorCPM following the same experimental protocol: reanalysis from 1980 to 2010 and seasonal and decadal predictions initialized from the reanalysis. We compare atmospheric initialization—Newtonian relaxation (nudging)—against ocean initialization—Ensemble Kalman Filter—(ODA). On the atmosphere, we explore the benefit of full-field (NudF-UVT) or anomaly (NudA-UVT) nudging of horizontal winds and temperature (U, V, and T) observations. The scheme NudA-UV nudges horizontal winds to disentangle the role of wind-driven variability. The ODA+NudA-UV scheme is a first attempt at joint initialization of ocean and atmospheric components in NorCPM. During the reanalysis, atmospheric nudging improves the synchronization of the atmosphere and land components with the observed data. Conversely, ODA is more effective at synchronizing the ocean component with observations. The atmospheric nudging schemes are better at reproducing specific events, such as the rapid North Atlantic subpolar gyre shift. An abrupt climatological change using the NudA-UV scheme demonstrates that energy conservation is crucial when only assimilating winds. ODA outperforms atmospheric-initialized versions for S2D global predictions, while atmospheric nudging is preferable for accurately initializing phenomena in specific regions, with the technique’s benefit depending on the prediction’s temporal scale. For instance, atmospheric full-field initialization benefits the tropical Atlantic Niño at 1-month lead time, and atmospheric anomaly initialization benefits longer lead times, reducing hindcast drift. Combining atmosphere and ocean initialization yields sub-optimal results, as sustaining the ensemble’s reliability—required for ODA’s performance—is challenging with atmospheric nudging.

An improved memetic algorithm for multi-objective resource-constrained flexible job shop inverse scheduling problem: An application for machining workshop

Resource-constrained flexible job shop scheduling problems are commonly encountered in some manufacturing industries, and have been widely studied in recent years. However, traditional resource constrained flexible job shop scheduling problem rarely consider the uncertainties in actual manufacturing systems, which may make the original schedule become suboptimal or even unfeasible. Therefore, a resource constrained flexible job shop inverse scheduling problem (RCFJISP) is proposed in this paper, which aims to cope with uncertain events by simultaneously adjusting the machine, worker and process parameters of the original schedule. A multi-objective optimization model is constructed to minimize the makespan, worker cost, machine energy consumption and deviation index. Furthermore, an improved memetic algorithm (IMA) is developed for solving the proposed problem. In IMA, a novel double-layer encoding mechanism is designed to enhance the capacity in exploring new solution’s domains. Three initialization strategies utilizing original scheduling information are designed to improve the quality of initial solutions. An adaptive mutation strategy and a local search mechanism are designed to enhance exploration and exploitation ability of the algorithm. And a crowding operator is proposed to reflect the diversity of the population effectively. In computational experiments, 28 extended benchmarks are constructed, and the effectiveness of the proposed strategy and algorithm is verified by comparing IMA with its 4 variants and other 4 widely used algorithms. Finally, two inverse scheduling problems of a real-world hydraulic cylinder machining workshop under two uncertain situations are studied. The results demonstrate that IMA can effectively solve the actual inverse scheduling problem. With a slight adjustment to the original scheduling, it can reduce the makespan by 11.5%, the worker cost by 8.1% and the machine energy consumption by 27.9% on average.

Study on combustion mechanism of methanol/nitromethane based on reactive molecular dynamics simulation

Oxygenated fuels and nitro fuels are effective strategies for addressing incomplete combustion by increasing the oxygen-fuel ratio. A computational approach based on reactive molecular dynamics simulations reveals oxidation mechanisms of temperature-induced effects on a mixed fuel of methanol and nitromethane. The method enables the demonstration of the initial reaction scheme of a binary fuel mixture, even for complicated interplay and coupling reactions. The results showed that the first reaction step of nitromethane was homolysis in poor-oxygen conditions, mainly via CH3NO2 → CH3 + NO2 (Net flux = 183, ratio = 92.89%). Methanol undergoes dehydrogenation reaction with the assistance of active radicals (OH, HO2, CH3, NO, and NO2), and the participation rates of these active groups are 60%, 17.14%, 8.57%, 7.62%, and 6.67%, respectively. The decomposition of nitromethane provided NO2 and CH3 radicals and significantly increased the amount of OH and NO via a reaction of NO2 + H → HNO2 → OH + NO. By fragment analysis, the main C1 intermediates are formed by pyrolysis of methanol/nitromethane such as formaldehyde, hydroxymethyl radical, and formyl radical. The CH3OH and CH2O are relatively stable, and the dehydrogenation is mainly highly active groups such as OH and NO. In contrast, the dehydrogenation of CH2OH and CHO free radicals is completed by self-cleavage or with the help of O2, NO2, etc. Our findings shed light on the oxidation behaviors of methanol/nitromethane mixed fuel in combustion.

Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification

We present a deep Graph Convolutional Kernel Machine (GCKM) for semi-supervised node classification in graphs. The method is built of two main types of blocks: (i) We introduce unsupervised kernel machine layers propagating the node features in a one-hop neighborhood, using implicit node feature mappings. (ii) We specify a semi-supervised classification kernel machine through the lens of the Fenchel-Young inequality. We derive an effective initialization scheme and efficient end-to-end training algorithm in the dual variables for the full architecture. The main idea underlying GCKM is that, because of the unsupervised core, the final model can achieve higher performance in semi-supervised node classification when few labels are available for training. Experimental results demonstrate the effectiveness of the proposed framework.

Leveraging Imagery Data with Spatial Point Prior for Weakly Semi-supervised 3D Object Detection

Training high-accuracy 3D detectors necessitates massive labeled 3D annotations with 7 degree-of-freedom, which is laborious and time-consuming. Therefore, the form of point annotations is proposed to offer significant prospects for practical applications in 3D detection, which is not only more accessible and less expensive but also provides strong spatial information for object localization. In this paper, we empirically discover that it is non-trivial to merely adapt Point-DETR to its 3D form, encountering two main bottlenecks: 1) it fails to encode strong 3D prior into the model, and 2) it generates low-quality pseudo labels in distant regions due to the extreme sparsity of LiDAR points. To overcome these challenges, we introduce Point-DETR3D, a teacher-student framework for weakly semi-supervised 3D detection, designed to fully capitalize on point-wise supervision within a constrained instance-wise annotation budget. Different from Point-DETR which encodes 3D positional information solely through a point encoder, we propose an explicit positional query initialization strategy to enhance the positional prior. Considering the low quality of pseudo labels at distant regions produced by the teacher model, we enhance the detector's perception by incorporating dense imagery data through a novel Cross-Modal Deformable RoI Fusion (D-RoI). Moreover, an innovative point-guided self-supervised learning technique is proposed to allow for fully exploiting point priors, even in student models. Extensive experiments on representative nuScenes dataset demonstrate our Point-DETR3D obtains significant improvements compared to previous works. Notably, with only 5% of labeled data, Point-DETR3D achieves over 90% performance of its fully supervised counterpart.

Model‐free learning‐based distributed cooperative tracking control of human‐in‐the‐loop multi‐agent systems

AbstractThis article studies the model‐free learning‐based distributed cooperative tracking control of human‐in‐the‐loop multi‐agent systems in the presence of an active leader. The core role of human‐in‐the‐loop is to use the ground station to send control commands to the non‐zero control input of the leader, and then directly or indirectly control a group of agents to complete complex tasks. Meanwhile, three essential demands including the completely unknown system model, the control objective obtained optimally, as well as no initial admissible control strategy requirement, are satisfied simultaneously. It is worth emphasizing that the relevant results only satisfy one or two demands at most, which are essentially not applicable to this problem. In this article, a model‐based human‐in‐the‐loop learning algorithm is first presented to achieve the optimal tracking control, as well as the convergence of the proposed learning algorithm is proved. Then, a bias‐based data‐driven learning algorithm is proposed, which provides the potential opportunities to overcome the difficulties caused by the above‐mentioned three demands. Finally, the validity of theoretical results is testified by a numerical example.

Formal Grammars Generating Fractal Descriptions of Molecular Structures

Simple rewriting rules are used to produce alphanumeric strings that embed fractal number sequences and are directly translatable into descriptions of hydrocarbon structures of considerable complexity, featuring hierarchical schemes. Rotations of the alphanumeric strings lead to radical rearrangements of the corresponding structures, which lose their initial schemes and become much less predictable, featuring different topologies of polygonal cycles. This shows that a complex and not necessarily ordered molecular structure may nevertheless have a relatively low algorithmic complexity. The variety and versatility of reorganization in chemical topology, due to the nonlocal representation of bonds in the coding string, may have played a role in prebiotic chemistry.

Modeling and multi-objective optimization for energy-aware scheduling of distributed hybrid flow-shop

With the development of economic globalization and sustainable manufacturing, energy-aware scheduling of distributed manufacturing systems has become a research hot topic. However, energy-aware scheduling of distributed hybrid flow-shop is rarely explored. Thus, this paper is the first attempt to study an energy-aware distributed hybrid flow-shop scheduling problem (DHFSP). We formulate a novel mathematical model of the DHFSP with minimizing makespan and total energy consumption (TEC) criteria. A hybrid multi-objective iterated greedy (HMOIG) approach is proposed to address this energy-aware DHFSP. In this proposed HMOIG, firstly, a new energy-saving method is presented and introduced into the model for reducing TEC criterion. Secondly, an integration initialization scheme is devised to produce initial solutions with high quality. Thirdly, two properties of DHFSP are used to invent a knowledge-based local search operator. Finally, we validate the effectiveness of each improvement component of HMOIG and compare it with other well-known multi-objective evolutionary algorithms on instances and a real-world case. Experimental results manifest that HMOIG is a promising method to solve this energy-aware DHFSP.

Personalizing renal replacement therapy initiation in the intensive care unit: a reinforcement learning-based strategy with external validation on the AKIKI randomized controlled trials.

The timely initiation of renal replacement therapy (RRT) for acute kidney injury (AKI) requires sequential decision-making tailored to individuals' evolving characteristics. To learn and validate optimal strategies for RRT initiation, we used reinforcement learning on clinical data from routine care and randomized controlled trials. We used the MIMIC-III database for development and AKIKI trials for validation. Participants were adult ICU patients with severe AKI receiving mechanical ventilation or catecholamine infusion. We used a doubly robust estimator to learn when to start RRT after the occurrence of severe AKI for three days in a row. We developed a "crude strategy" maximizing the population-level hospital-free days at day 60 (HFD60) and a "stringent strategy" recommending RRT when there is significant evidence of benefit for an individual. For validation, we evaluated the causal effects of implementing our learned strategies versus following current best practices on HFD60. We included 3748 patients in the development set and 1068 in the validation set. Through external validation, the crude and stringent strategies yielded an average difference of 13.7 [95% CI -5.3 to 35.7] and 14.9 [95% CI -3.2 to 39.2] HFD60, respectively, compared to current best practices. The stringent strategy led to initiating RRT within 3 days in 14% of patients versus 38% under best practices. Implementing our strategies could improve the average number of days that ICU patients spend alive and outside the hospital while sparing RRT for many. We developed and validated a practical and interpretable dynamic decision support system for RRT initiation in the ICU.

Multi-objective optimization of residential building energy consumption, daylighting, and thermal comfort based on BO-XGBoost-NSGA-II

The energy consumption, daylighting, and thermal comfort of buildings directly affect the three key goals of residents. However, there is little research on the optimization of energy consumption, daylighting, and thermal comfort in residential buildings in China. Therefore, this study proposes an optimization framework that combines Bayesian optimization with extreme gradient boosting trees (BO-XGBoost) and non-dominated genetic algorithm-II (NSGA-II) to study the multi-objective optimization of residential building performance. This paper first uses Grasshopper to simulate and obtain a dataset through Latin hypercube sampling (LHS). BO-XGBoost is used to establish the regression relationship between building envelope design parameters and residential building performance. Then, the obtained regression model is used as the fitness function of NSGA-II to get the Pareto optimal solution set. Finally, the ideal point method is used to obtain the optimal combination of building envelope structure parameters for residential buildings. Taking a residential building in a hot summer and cold winter area as an example, the effectiveness of this method is verified. The results show that (1) BO-XGBoost has excellent predictive performance, with R2 values of 0.997, 0.960, and 0.994 for energy consumption, thermal comfort, and daylighting, respectively. (2) The proposed BO-XGBoost-NSGA-II can effectively achieve multi-objective optimization. Compared with the initial scheme of the case building, energy consumption is reduced by 44.1%, thermal comfort index is reduced by 10.9%, and daylighting performance is improved by 1.7%. Therefore, the proposed method can effectively optimize the performance goals of residential buildings and provide practical ideas for similar problems.

Revealing the Impact of Investment Benefits on Marketing Decision in Public Infrastructures Based on Game Theory: Case Study of Large-Scale Exhibition Infrastructures in China

Investing in large-scale public infrastructures is vital for urban renewal and development, often relying on specific types of buildings to drive investment in municipal infrastructure and supporting service buildings. However, the complex interplay of interests among investors introduces unpredictability, hindering the effectiveness of such investments. This study employs evolutionary game theory to explore how investment benefits influence investor behavior. Using China’s large-scale exhibition infrastructures as a case study, a tripartite evolutionary game model is developed, involving the government, investors in exhibition buildings, and investors in supporting services. The strategies of the three parties are analyzed, and simulations explore the effects of different initial strategy values, costs, subsidies, and profits. Five research outcomes are identified, indicating that favorable initial investments and optimal subsidies encourage proactive investment, while high initial costs deter it. Consequently, five recommendations for promoting coordinated investment in exhibition facilities are proposed, including the establishment of communication platforms to enhance stakeholder cooperation. These findings offer insights for decision making in other large-scale infrastructure investments, such as sports facilities and transportation hubs.

Desirability of Outcome Ranking and Response Adjusted for Antibiotic Risk (DOOR/RADAR) Post Hoc Analysis Supports Equipoise for Antibiotic Initiation Strategies in Intensive Care Unit-Acquired Pneumonia.

Background: Pneumonia is the most common intensive care unit (ICU)-acquired infection and source of potential sepsis in ICU populations but can be difficult to diagnose in real-time. Despite limited data, rapid initiation of antibiotic agents is endorsed by society guidelines. We hypothesized that a post hoc analysis of a recent randomized pilot study would show no difference between two antibiotic initiation strategies. Patients and Methods: The recent Trial of Antibiotic Restraint in Presumed Pneumonia (TARPP) was a pragmatic cluster-randomized pilot of antibiotic initiation strategies for patients with suspected ICU-acquired pneumonia. Participating ICUs were cluster-randomized to either an immediate initiation protocol or a specimen-initiated protocol where a gram stain was required for initiation of antibiotics. Patients in the study were divided into one of seven mutually exclusive outcome rankings (desirability of outcome ranking; DOOR): (1) Survival, No Pneumonia, No adverse events; (2) Survival, Pneumonia, No adverse events; (3) Survival, No Pneumonia, ventilator-free-alive days ≤14; (4) Survival, Pneumonia, ventilator-free-alive days ≤14; (5) Survival, No Pneumonia, Subsequent episode of suspected pneumonia; (6) Survival, Pneumonia, Subsequent episode of suspected pneumonia; and (7) Death. These rankings were further refined using the duration of antibiotics prescribed for pneumonia (response adjusted for antibiotic risk; RADAR). Results: There were 186 patients enrolled in the study. After applying the DOOR analysis, a randomly selected patient was equally likely to have a better outcome in specimen-initiated arm as in the immediate initiation arm (DOOR probability: 50.8%; 95% confidence interval [CI], 42.7%-58.9%). Outcome probabilities were similar after applying the RADAR analysis (52.5%; 95% CI, 44.2%-60.6%; p = 0.31). Conclusions: We found that patients for whom antibiotic agents were withheld until there was objective evidence (specimen-initiated group) had similar outcome rankings to patients for whom antibiotic agents were started immediately. This supports the findings of the TARPP pilot trial and provides further evidence for equipoise between these two treatment strategies.