Current Iteration Research Articles

Ulster’s Evangelical Plain Style: A Visual Inquiry

The paper explores the design of the small evangelical halls that exist across the nine counties of Ulster. Conceived of as conduits to salvation, as representing the current non-material iteration of God’s holy temple, and even as prefiguring the coming eschaton, these are simple and unassuming buildings, decorated largely with text. The paper argues that the “plain style” provides a fruitful approach to consideration of the materiality of these spaces. Plain style is defined as a complex denial of ostentation and elaboration in design and communication that is linked to reformed Protestantism. Contending that such a plain style must engage with the quotidian and the contingent, the importance and the problems of photo-documentation as a method of inquiry are discussed. The paper argues that a careful and reflexive approach to photography and analysis, drawing on keywording and metadata provides a method suitable for exploration of such “new” spaces where access must be negotiated with care. The paper concludes by presenting some findings drawn from a keywording approach to the visual data and relating the use of text, materials and building forms to discussion of an everyday plain style.

Seeing Resilience in Students: Information Literacy as a Social Practice for Academic Instruction Librarians

ABSTRACTThis poster will describe a dissertation project that consisted of a questionnaire and interview phase followed by a focus group and semi structured diary phase. This project investigated the theory‐to‐practice gap in seeing information literacy as a socially constructed practice among academic instruction librarians. Phase one consisted of 17 interviews that were coded and analyzed using grounded theory approaches. The second phase of the research introduced the resulting grounded theory to new participants. The participants then took part in two focus groups, one at the start of the semester and one at the end of the fall 2021 semester. Between the two focus groups participants completed semi structured diary entries related to their instruction sessions. Grounded theory approaches were used to modify the original theory into the current iteration.

"Stop Bombarding Us With Work We Don't Even Need": Reconceptualising a First-Year Course for Increased Relevance Through Action Learning

South African lecturers and students often struggle to reconcile the demands of an increasingly globalised world with pedagogies that can be adapted to harness students' local contexts, and which can be used to draw on students' lived experiences in the African context. This paper explores how a first-year academic literacies course could be reconceptualised to become more relevant to education students' lived realities, while also preparing them for a competitive degree and a career with global relevance. The current iteration of this course can be described as mainly theoretical with a typical Western course structure. Student feedback indicates that students fail to see the relevance of the course to their degrees and future careers. This conceptual paper considers how the course could be reimagined within a paradigm of action learning that is centred around project-based, socially embedded community-based learning to firstly, encourage students to draw on deep approaches to learning (Biggs, 1999) and secondly, to counter the effects of alienation (Mann, 2001) experienced by students. Ultimately, the paper hopes to offer an example of innovate course design that could successfully be implemented in resource-poor contexts.

"Stop Bombarding Us With Work We Don't Even Need": Reconceptualising a First-Year Course for Increased Relevance Through Action Learning

South African lecturers and students often struggle to reconcile the demands of an increasingly globalised world with pedagogies that can be adapted to harness students' local contexts, and which can be used to draw on students' lived experiences in the African context. This paper explores how a first-year academic literacies course could be reconceptualised to become more relevant to education students' lived realities, while also preparing them for a competitive degree and a career with global relevance. The current iteration of this course can be described as mainly theoretical with a typical Western course structure. Student feedback indicates that students fail to see the relevance of the course to their degrees and future careers. This conceptual paper considers how the course could be reimagined within a paradigm of action learning that is centred around project-based, socially embedded community-based learning to firstly, encourage students to draw on deep approaches to learning (Biggs, 1999) and secondly, to counter the effects of alienation (Mann, 2001) experienced by students. Ultimately, the paper hopes to offer an example of innovate course design that could successfully be implemented in resource-poor contexts.

CryptoSolve: Towards a Tool for the Symbolic Analysis of Cryptographic Algorithms

Recently, interest has been emerging in the application of symbolic techniques to the specification and analysis of cryptosystems. These techniques, when accompanied by suitable proofs of soundness/completeness, can be used both to identify insecure cryptosystems and prove sound ones secure. But although a number of such symbolic algorithms have been developed and implemented, they remain scattered throughout the literature. In this paper, we present a tool, CryptoSolve, which provides a common basis for specification and implementation of these algorithms, CryptoSolve includes libraries that provide the term algebras used to express symbolic cryptographic systems, as well as implementations of useful algorithms, such as unification and variant generation. In its current initial iteration, it features several algorithms for the generation and analysis of cryptographic modes of operation, which allow one to use block ciphers to encrypt messages more than one block long. The goal of our work is to continue expanding the tool in order to consider additional cryptosystems and security questions, as well as extend the symbolic libraries to increase their applicability.

Spectrally simplified approach for leveraging legacy geostationary oceanic observations.

The use of multispectral geostationary satellites to study aquatic ecosystems improves the temporal frequency of observations and mitigates cloud obstruction, but no operational capability presently exists for the coastal and inland waters of the United States. The Advanced Baseline Imager (ABI) on the current iteration of the Geostationary Operational Environmental Satellites, termed the R Series (GOES-R), however, provides sub-hourly imagery and the opportunity to overcome this deficit and to leverage a large repository of existing GOES-R aquatic observations. The fulfillment of this opportunity is assessed herein using a spectrally simplified, two-channel aquatic algorithm consistent with ABI wave bands to estimate the diffuse attenuation coefficient for photosynthetically available radiation, Kd(PAR). First, an in situ ABI dataset was synthesized using a globally representative dataset of above- and in-water radiometric data products. Values of Kd(PAR) were estimated by fitting the ratio of the shortest and longest visible wave bands from the in situ ABI dataset to coincident, in situKd(PAR) data products. The algorithm was evaluated based on an iterative cross-validation analysis in which 80% of the dataset was randomly partitioned for fitting and the remaining 20% was used for validation. The iteration producing the median coefficient of determination (R2) value (0.88) resulted in a root mean square difference of 0.319m-1, or 8.5% of the range in the validation dataset. Second, coincident mid-day images of central and southern California from ABI and from the Moderate Resolution Imaging Spectroradiometer (MODIS) were compared using Google Earth Engine (GEE). GEE default ABI reflectance values were adjusted based on a near infrared signal. Matchups between the ABI and MODIS imagery indicated similar spatial variability (R2=0.60) between ABI adjusted blue-to-red reflectance ratio values and MODIS default diffuse attenuation coefficient for spectral downward irradiance at 490 nm, Kd(490), values. This work demonstrates that if an operational capability to provide ABI aquatic data products was realized, the spectral configuration of ABI would potentially support a sub-hourly, visible aquatic data product that is applicable to water-mass tracing and physical oceanography research.

Instance elimination strategy for non-convex multiple-instance support vector machine

Multiple-instance support vector machine (SVM) is a classic and effective algorithm to address the classification of bags. However, training multiple-instance SVM is expensive. To deal with this issue, the safe screening rule is introduced to solve optimization problem, which can significantly mitigate the storage burden by identifying inactive instances. Specifically, an instance elimination strategy is designed for the inner solver of a signal concave–convex procedure (CCCP). Then, between the iterations of CCCP, due to the label of instance may change, to cope with this difficulty, a dual screening method with variational inequalities (DVI) is used to identify the part of inactive instances. To further speed up the solution efficiency, a smart dual coordinate descent method (SDCDM) is introduced for the inner solver, which skips over many updates that result in no change to the current iteration. Experiments on 35 benchmark datasets significantly exhibit the outstanding performance of the proposed strategy.

Development of a simulation technical competence curriculum for medical simulation fellows

Background and needsMedical educators with simulation fellowship training have a unique skill set. Simulation fellowship graduates have the ability to handle basic and common troubleshooting issues with simulation software, hardware, and equipment setup. Outside of formal training programs such as this, simulation skills are inconsistently taught and organically learned. This is important to address because there are high expectations of medical educators who complete simulation fellowships. To fill the gap, we offer one way of teaching and assessing simulation technical skills within a fellowship curriculum and reflect on lessons learned throughout the process. This report describes the instructional designs, implementation, and program evaluation of an educational intervention: a simulation technology curriculum for simulation fellows.Curriculum designThe current iteration of the simulation technical skill curriculum was introduced in 2018 and took approximately 8 months to develop under the guidance of expert simulation technology specialists, simulation fellowship-trained faculty, and simulation center administrators. Kern’s six steps to curriculum development was used as the guiding conceptual framework. The curriculum was categorized into four domains, which emerged from the outcome of a qualitative needs assessment. Instructional sessions occurred on 5 days spanning a 2-week block. The final session concluded with summative testing.Program evaluationFellows were administered summative objective structured exams at three stations. The performance was rated by instructors using station-specific checklists. Scores approached 100% accuracy/completion for all stations.ConclusionsThe development of an evidence-based educational intervention, a simulation technical skill curriculum, was highly regarded by participants and demonstrated effective training of the simulation fellows. This curriculum serves as a template for other simulationists to implement formal training in simulation technical skills.

Estimation of Cs-137 emissions during wildfires and dust storm in Chernobyl Exclusion Zone in April 2020 using ensemble iterative source inversion method

The emission inventories of cesium-137 resulting from the wildfires in the Chernobyl Exclusion Zone (3–24 April 2020) and from the dust storm (16–17 April 2020), which resuspended contaminated ash, were estimated using inverse modeling. The goal of this work was to take into account uncertainties of inexactly known source term and meteorological input data for evaluation of emission inventories and their confidence limits, by developing Ensemble Iterative Source Inversion Method (EISIM). A set of source receptor matrices (SRMs) was calculated using FLEXPART atmospheric transport code with varying source term parameters (size distribution of emitted particles, height distribution of emissions) and meteorological input data. The covariance matrix of model errors was estimated by ensemble averaging of model results obtained after multiplication of the pre-calculated SRMs by the estimated emission inventories at the current iteration step. The emission inventories at each iteration step were evaluated for each of the ensemble members by solving the conventional variational source inversion problem. With iterations, the variance of model error was reduced by an order of magnitude. The estimated total emission of 137Cs from wildfires was 448 GBq, close to the first guess estimation. By using emission inventories within the obtained confidence limits (from 39 to 1530 GBq), different combinations of source term parameters and input meteorological data, FLEXPART could fit observations with a correlation coefficient of more than 0.6 and a normalized mean squared error of less than 10. The obtained estimate of the total emission resulting from the wind resuspension during the dust storm was 27 GBq. The respective estimated confidence interval was from 3 to 93 GBq. By analyzing model error statistics, some of the source term parameters could be reliably evaluated. The fraction of the fine particles (0.25 μm) in total emissions Wsf(0.25)≈0.1 and the fraction of emission below a bottom height of convective plume Wh(1)≈0.5.

Advanced Simulation-Inserted Optimization Using Combined Quasi-Newton Method With Lagrangian Method for EM-Based Design Optimization

Traditional electromagnetic (EM)-based design optimizations typically treat the EM simulation as a black-box and use optimization algorithms to externally drive EM simulations iteratively. Recently, a novel EM-based design optimization technique, named as simulation-inserted optimization (SIO) in this article, has been developed by opening the black-box and inserting the optimization algorithm into the internal of EM simulation to achieve EM field solutions and the optimal values of design variables simultaneously. This article proposes an advanced SIO algorithm using combined quasi-Newton method with Lagrangian method. In the proposed algorithm, the finite element method (FEM) is used as the EM simulation method, and the Lagrangian method is incorporated to internally integrate EM simulation with design optimization. New formulations based on quasi-Newton method are derived to approximate the computationally intensive lower upper (LU) decomposition of the FEM system matrix in the current optimization iteration by that in the previous optimization iteration to significantly reduce the number of LU decompositions. The proposed SIO technique further derives novel optimization update formulations by introducing the line search algorithm to increase the robustness of optimization. Using the proposed SIO algorithm, only a few initial LU decompositions of FEM system matrices need to be calculated instead of repetitively calculating a large number of LU decompositions of FEM system matrices during optimization, consequently speeding up the overall optimization process. The proposed technique is demonstrated by two application examples of EM-based design optimization of microwave components.

The Evolution of an Innovative Online Task to Monitor Children's Oral Narrative Development.

Oral narrative abilities are an important measure of children's language competency and have predictive value for children's later academic performance. Research and development underway in New Zealand is advancing an innovative online oral narrative task. This task uses audio recordings of children's story retells, speech-to-text software and language analysis to record, transcribe, analyse and present oral narrative and listening comprehension data back to class teachers. The task has been designed for class teachers' use with the support of SLP or literacy specialists in data interpretation. Teachers are upskilled and supported in order to interpret these data and implement teaching practices for students through online professional learning and development modules, within the context of a broader evidence-based approach to early literacy instruction. This article describes the development of this innovative, culturally relevant, online tool for monitoring children's oral narrative ability and listening comprehension in their first year of school. Three phases of development are outlined, showing the progression of the tool from a researcher-administered task during controlled research trials, to wide-scale implementation with thousands of students throughout New Zealand. The current iteration of the tool uses an automatic speech-recognition system with specifically trained transcription models and support from research assistants to check transcription, then code and analyse the oral narrative. This reduces transcription and analysis time to ~7 min, with a word error rate of around 20%. Future development plans to increase the accuracy of automatic transcription and embed basic language analysis into the tool, with the aim of removing the need for support from research assistants.

Development and characterization of a DMAPS chip in TowerJazz [formula omitted] [formula omitted] technology for high radiation environments

The increasing availability of commercial CMOS processes with high-resistivity wafers has fueled the R&D of depleted monolithic active pixel sensors (DMAPS) for use in high energy physics experiments. One of these developments is a series of monolithic pixel detectors with column-drain readout architecture and small collection electrode allowing for low-power designs (TJ-Monopix). It is designed in a 180nm TowerJazz CMOS process and features a pixel size of 33μm×33μm. The efforts and improvements on the front-end electronics and sensor design of the current iteration TJ-Monopix2 increase the radiation hardness and efficiency while lowering the threshold and noise.

Iterative fault estimation and fault‐tolerant control for a class of nonlinear variant time‐delay systems

AbstractFault estimation (FE) and fault‐tolerant control (FTC) are remarkable techniques, have achieved great success in many applications such as robot, spacecraft, and industrial assembly line. This article aims to design an iterative‐learning scheme based FE and FTC method for a class of nonlinear system with iteration‐variant state delay and additive measurement noise. A Luenberger observer in iterative version is proposed to achieve the reconstruction of system state information, which consider the historical observation error in order to improve the observation performance in current iteration. To deal with bounded iteration‐variant state delay, an iterative‐learning scheme based fault estimator is designed and the convergence is proved. Compared with relevant methods which use system output observation residual to revise the FE result of last iteration, the proposed approach uses filtered system output observation residual in order to reduce the effect of measurement noise. Based on the FE result, FTC using signal compensation technique is employed. In addition, an improved particle swarm optimization algorithm is employed for parameters adaptive tuning. Compared with traditional manual adjustment of parameters, the proposed method can find the optimal parameters and save time of parameter tuning. Finally, three examples are provided to verify the effectiveness of the proposed approach.

PROBLEM OF CLASSIFICATION OF SEMANTIC KERNELS OF WEB RESOURCE

The article presents a new theoretical basis for solving the problem of situational management of semantic cores identified on the basis of WEB content. Such a task arises within the framework of a new phenomenon called virtual promotion. Its essence lies in the fact that a real product can exist in two realities: online and offline. According to marketing theory, the lifetime in two realities is the same. However, in the online mode, the goods exist independently and in accordance with the laws of the use of Internet technologies. Therefore, based on the concept of a marketing channel, it was proposed to consider a message in such a channel as a semantic core. The core is a specially selected set of keywords that briefly describe the product and the corresponding need. It has been proposed that each need forms a so-called class of need. Therefore, the product description will either belong to this class or not. In addition, a product can be described by a different set of keywords, which means that different descriptions of the same product or several products, if there are any for sale in the enterprise, will fall into the demand class. As a result, in this work, it was proposed to consider the center of this class as the so-called K-candidate. It is the K-applicant that will be the semantic core that will be considered at the current iteration of the situational management process. In addition, in order to move from one situation to another, in other words, from one core to another, it is required to have such an alternative core. It can be safely taken either from the neighborhood of the need class center (K-applicant), or the center of another class (another K-applicant), if the product can cover several needs of a potential buyer. Then the actual task is to classify the classes of needs based on the text corpus in HTML format. Having a text corpus at the first stage, the task of synthesizing semantic cores is realized, and then the classification task itself. This article proposes the formulation of the classification problem, taking into account the features that the Internet technologies contribute to search engine optimization. In particular, it is proposed to use four metrics from the category of WEB statistics. And then it is proposed to use the clustering method to identify classes of needs, taking into account the fact that the K-applicant is presented as a semantic network or as a graph.

An efficient gradient method with approximately optimal stepsizes based on regularization models for unconstrained optimization

It is widely accepted that the stepsize is of great significance to gradient method. An efficient gradient method with approximately optimal stepsizes mainly based on regularization models is proposed for unconstrained optimization. More specifically, if the objective function is not close to a quadratic function on the line segment between the current and latest iterates, regularization model is exploited carefully to generate approximately optimal stepsize. Otherwise, quadratic approximation model is used. In addition, when the curvature is non-positive, special regularization model is developed. The convergence of the proposed method is established under some weak conditions. Extensive numerical experiments indicated the proposed method is very promising. Due to the surprising efficiency, we believe that gradient methods with approximately optimal stepsizes can become strong candidates for large-scale unconstrained optimization.

Utilizing Healthy People 2030 to Engage Leadership, Key Constituents, and the Public Across Multiple Sectors: Opportunities for Local Health Departments and Community Partners.

Utilizing Healthy People 2030 to Engage Leadership, Key Constituents, and the Public Across Multiple Sectors: Opportunities for Local Health Departments and Community Partners.

Cripistemologies and resisting the calls to return to normal

The purpose of this article is to engage crip theory in a critical analysis of the calls within elementary education for a return to normalcy. I seek to question the ways Covid-19 has reinforced orientations towards normalcy by asking where normalcy went and how the calls for its return reveal the fundamental limits of inclusion within schools. Uses of the terms normalcy, normal, and normative within the context of this article refer to the mythical white, male, able-bodied, middle-class, heteronormative figure that remains hegemonic as well as widely resisted, questioned, and critiqued within critical disability studies. Through the application of crip theory and cripistemologies, I contend in this article that calls to return to normally engage in a persistent effort to erase and exclude disabled children and youth from a potentially transformative and necessary conversation about how we might pursue conceptualizations and enactments of inclusion outside of its current adherence to normative neoliberal aims and objectives. Rather than accepting the conditions of inclusion (i.e., the sustaining of normalcy) in its current neoliberal iteration, in this article I invite educators to crip calls for inclusion and crip calls for a return to normal.

A Message Passing-Assisted Iterative Noise Cancellation Method for Clipped OTFS-BFDM Systems.

Compared with orthogonal frequency division multiplexing (OFDM) systems, orthogonal time frequency space systems based on bi-orthogonal frequency division multiplexing (OTFS-BFDM) have lower out-of-band emission (OOBE) and better robustness to high-mobility scenarios, but suffer from a higher peak-to-average ratio (PAPR) in large data packets. In this paper, one-iteration clipping and filtering (OCF) is adopted to reduce the PAPR of OTFS-BFDM signals. However, the extra noise introduced by the clipping process, i.e., clipping noise, will distort the desired signal and increase the bit error rate (BER). We propose a message passing (MP)-assisted iterative cancellation (MP-AIC) method to cancel the clipping noise based on the traditional MP decoding at the receiver, which incorporates with the (OCF) at the transmitter to keep the sparsity of the effective channel matrix. The main idea of MP-AIC is to extract the residual signal fed to the MP detector by iteratively constructing reference clipping noise at the receiver. During each iteration, the variance of residual signal and channel noise are taken as input parameters of MP decoding to improve the BER. Moreover, the convergence probability of the modulation alphabet after MP decoding in the current iteration is used as the initial probability of MP decoding in the next iteration to accelerate the convergence rate of MP decoding. Simulation results show that the proposed MP-AIC method significantly improves MP-decoding accuracy while accelerating the BER convergence in the clipped OTFS-BFDM system. In the clipped OTFS-BFDM system with rectangular pulse shaping, the BER of MP-AIC with two iterations can be reduced by 72% more than that without clipping noise cancellation.

Sparse seismic reflectivity inversion using an adaptive fast iterative shrinkage‐thresholding algorithm

ABSTRACTSeismic reflectivity inversion using l1‐norm regularization produces sparse solutions by applying an l1‐norm constraint. The fast iterative shrinkage‐thresholding algorithm is one of the most effective methods to solve l1‐norm regularized inversion problems. A large number of iterations are commonly required in the fast iterative shrinkage‐thresholding algorithm because its solution converges slowly towards the sparse solution. To improve its convergence rate, we introduce a modifying strategy for the traditional fast iterative shrinkage‐thresholding algorithm. When implementing the soft‐thresholding operator, the thresholding value is adaptively adjusted by assigning the reciprocal of the solution in the previous iteration as a weight to the thresholding value of the current iteration. In this way, small variables in the solution produce large coefficients applied to the thresholding value, which causes small variables to quickly converge to zero. The adaptive fast iterative shrinkage‐thresholding algorithm shows significantly improved computational efficiency and accuracy compared to the traditional fast iterative shrinkage‐thresholding algorithm. It produces good results for both numerical and field examples of l1‐norm regularized seismic reflectivity inversion.

Does the SORG Orthopaedic Research Group Hip Fracture Delirium Algorithm Perform Well on an Independent Intercontinental Cohort of Patients With Hip Fractures Who Are 60 Years or Older?

Postoperative delirium in patients aged 60 years or older with hip fractures adversely affects clinical and functional outcomes. The economic cost of delirium is estimated to be as high as USD 25,000 per patient, with a total budgetary impact between USD 6.6 to USD 82.4 billion annually in the United States alone. Forty percent of delirium episodes are preventable, and accurate risk stratification can decrease the incidence and improve clinical outcomes in patients. A previously developed clinical prediction model (the SORG Orthopaedic Research Group hip fracture delirium machine-learning algorithm) is highly accurate on internal validation (in 28,207 patients with hip fractures aged 60 years or older in a US cohort) in identifying at-risk patients, and it can facilitate the best use of preventive interventions; however, it has not been tested in an independent population. For an algorithm to be useful in real life, it must be valid externally, meaning that it must perform well in a patient cohort different from the cohort used to "train" it. With many promising machine-learning prediction models and many promising delirium models, only few have also been externally validated, and even fewer are international validation studies. Does the SORG hip fracture delirium algorithm, initially trained on a database from the United States, perform well on external validation in patients aged 60 years or older in Australia and New Zealand? We previously developed a model in 2021 for assessing risk of delirium in hip fracture patients using records of 28,207 patients obtained from the American College of Surgeons National Surgical Quality Improvement Program. Variables included in the original model included age, American Society of Anesthesiologists (ASA) class, functional status (independent or partially or totally dependent for any activities of daily living), preoperative dementia, preoperative delirium, and preoperative need for a mobility aid. To assess whether this model could be applied elsewhere, we used records from an international hip fracture registry. Between June 2017 and December 2018, 6672 patients older than 60 years of age in Australia and New Zealand were treated surgically for a femoral neck, intertrochanteric hip, or subtrochanteric hip fracture and entered into the Australian & New Zealand Hip Fracture Registry. Patients were excluded if they had a pathological hip fracture or septic shock. Of all patients, 6% (402 of 6672) did not meet the inclusion criteria, leaving 94% (6270 of 6672) of patients available for inclusion in this retrospective analysis. Seventy-one percent (4249 of 5986) of patients were aged 80 years or older, after accounting for 5% (284 of 6270) of missing values; 68% (4292 of 6266) were female, after accounting for 0.06% (4 of 6270) of missing values, and 83% (4690 of 5661) of patients were classified as ASA III/IV, after accounting for 10% (609 of 6270) of missing values. Missing data were imputed using the missForest methodology. In total, 39% (2467 of 6270) of patients developed postoperative delirium. The performance of the SORG hip fracture delirium algorithm on the validation cohort was assessed by discrimination, calibration, Brier score, and a decision curve analysis. Discrimination, known as the area under the receiver operating characteristic curves (c-statistic), measures the model's ability to distinguish patients who achieved the outcomes from those who did not and ranges from 0.5 to 1.0, with 1.0 indicating the highest discrimination score and 0.50 the lowest. Calibration plots the predicted versus the observed probabilities, a perfect plot has an intercept of 0 and a slope of 1. The Brier score calculates a composite of discrimination and calibration, with 0 indicating perfect prediction and 1 the poorest. The SORG hip fracture algorithm, when applied to an external patient cohort, distinguished between patients at low risk and patients at moderate to high risk of developing postoperative delirium. The SORG hip fracture algorithm performed with a c-statistic of 0.74 (95% confidence interval 0.73 to 0.76). The calibration plot showed high accuracy in the lower predicted probabilities (intercept -0.28, slope 0.52) and a Brier score of 0.22 (the null model Brier score was 0.24). The decision curve analysis showed that the model can be beneficial compared with no model or compared with characterizing all patients as at risk for developing delirium. Algorithms developed with machine learning are a potential tool for refining treatment of at-risk patients. If high-risk patients can be reliably identified, resources can be appropriately directed toward their care. Although the current iteration of SORG should not be relied on for patient care, it suggests potential utility in assessing risk. Further assessment in different populations, made easier by international collaborations and standardization of registries, would be useful in the development of universally valid prediction models. The model can be freely accessed at: https://sorg-apps.shinyapps.io/hipfxdelirium/ . Level III, therapeutic study.