Iterative Search Research Articles

3D-localization of single point-like gamma sources with a coded aperture camera

Objective. 3D-localization of gamma sources has the potential to improve the outcome of radio-guided surgery. The goal of this paper is to analyze the localization accuracy for point-like sources with a single coded aperture camera. Approach. We both simulated and measured a point-like 241 Am source at 17 positions distributed within the field of view of an experimental gamma camera. The setup includes a 0.11mm thick Tungsten sheet with a MURA mask of rank 31 and pinholes of 0.08mm in diameter and a detector based on the photon counting readout circuit Timepix3. Two methods, namely an iterative search including either a symmetric Gaussian fitting or an exponentially modified Gaussian fitting (EMG) and a center of mass method were compared to estimate the 3D source position. Main results. Considering the decreasing axial resolution with source-to-mask distance, the EMG improved the results by a factor of 4 compared to the Gaussian fitting based on the simulated data. Overall, we obtained a mean localization error of 0.77mm on the simulated and 2.64mm on the experimental data in the imaging range of 20−100mm . Significance. This paper shows that despite the low axial resolution, point-like sources in the nearfield can be localized as well as with more sophisticated imaging devices such as stereo cameras. The influence of the source size and the photon count on the imaging and localization accuracy remains an important issue for further research.

Study on the Determination of Flavor Value of Rice Based on Grid Iterative Search Swarm Optimization Support Vector Machine Model and Hyperspectral Imaging.

In the field of rice processing and cultivation, it is crucial to adopt efficient, rapid and user-friendly techniques to detect the flavor values of various rice varieties. The conventional methods for flavor value assessment mainly rely on chemical analysis and technical evaluation, which not only deplete the rice resources but also incur significant time and labor costs. In this study, hyperspectral imaging technology was utilized in combination with an improved Particle Swarm Optimization Support Vector Machine (PSO-SVM) algorithm, i.e., the Grid Iterative Search Particle Swarm Optimization Support Vector Machine (GISPSO-SVM) algorithm, introducing a new non-destructive technique to determine the flavor value of rice. The method captures the hyperspectral feature data of different rice varieties through image acquisition, preprocessing and feature extraction, and then uses these features to train a model using an optimized machine learning algorithm. The results show that the introduction of GIS algorithms in a PSO-optimized SVM is very effective and can improve the parameter finding ability. In terms of flavor value prediction accuracy, the Principal Component Analysis (PCA) combined with the GISPSO-SVM algorithm achieved 96% accuracy, which was higher than the 93% of the Competitive Adaptive Weighted Sampling (CARS) algorithm. And the introduction of the GIS algorithm in different feature selection can improve the accuracy to different degrees. This novel approach helps to evaluate the flavor values of new rice varieties non-destructively and provides a new perspective for future rice flavor value detection methods.

Altitude optimality boundary of two variants of large space debris removal to disposal orbits

The article considers the mission design problem for removal of large space debris objects from near-Earth orbits. It is assumed that a single active spacecraft-collector (SC) should de/re-orbit several objects. The upper planning level calls for a scenario that specifies the type of repeating operations of an SC. Despite the apparent diversity of these scenarios, they can be reduced to two variants of objects removal to disposal orbits (DOs). In variant I, transfers between objects are effected by an SC with detachable propulsion modules (PMs) onboard. Once the next-in-line object is captured, one module is accommodated on its surface, and, after demating, this PM ensures the relocation of the object to a DO. In variant II, the SC operates as a tug vehicle and transfers the object to DO on its own, and then returns to the next object.A reasonable choice of the removal variant depends on the SC launch mass which should be compared for the same number of de/re-orbited derelict targets. To this end, one should estimate the SC dry mass, the payload mass, and the SC propellant mass. Each of these three terms is nonlinear and depends explicitly on each other, the mass of the captured object, and on the SC maneuvers. Analytical models are proposed for estimation of the SC launch mass for each of the two removal variants. It is also shown that there exists an altitude optimality boundary (the radius of a spherical layer) above which the removal variant II is more advantageous than variant I. Such models are constructed based on several assumptions capable of breaking the iterative search of the SC launch mass.The position of the altitude optimality boundary depends on seven input parameters of the mission: the SC dry mass; the PM dry mass; the mass of the object, the required variation of the semi-major axis Δa to reach the DO, the number of objects removed by a single SC, the average ΔV for a transfer between two objects, and the effective exhaust velocity of SC main engine. In the present paper, four types of objects are considered: heavy and light LV stages in low orbits, upper stages in GEO and GNSS regions. By choosing concrete objects of study one can substantially reduce the solution space by excluding the impossible combinations of input data. By using the developed SC launch mass models, it is possible to formulate removal recommendations for concrete groups of space debris objects. These models were shown as being adequate on the examples of Elsa-M and MRV projects which are due to be implemented in the next one-two years.

Microbial metaproteomics--From sample processing to data acquisition and analysis

Microorganisms are closely associated with human diseases and health. Understanding the composition and function of microbial communities requires extensive research. Metaproteomics has recently become an important method for throughout and in-depth study of microorganisms. However, major challenges in terms of sample processing, mass spectrometric data acquisition, and data analysis limit the development of metaproteomics owing to the complexity and high heterogeneity of microbial community samples. In metaproteomic analysis, optimizing the preprocessing method for different types of samples and adopting different microbial isolation, enrichment, extraction, and lysis schemes are often necessary. Similar to those for single-species proteomics, the mass spectrometric data acquisition modes for metaproteomics include data-dependent acquisition (DDA) and data-independent acquisition (DIA). DIA can collect comprehensive peptide information from a sample and holds great potential for future development. However, data analysis for DIA is challenged by the complexity of metaproteome samples, which hinders the deeper coverage of metaproteomes. The most important step in data analysis is the construction of a protein sequence database. The size and completeness of the database strongly influence not only the number of identifications, but also analyses at the species and functional levels. The current gold standard for metaproteome database construction is the metagenomic sequencing-based protein sequence database. A public database-filtering method based on an iterative database search has been proven to have strong practical value. The peptide-centric DIA data analysis method is a mainstream data analysis strategy. The development of deep learning and artificial intelligence will greatly promote the accuracy, coverage, and speed of metaproteomic analysis. In terms of downstream bioinformatics analysis, a series of annotation tools that can perform species annotation at the protein, peptide, and gene levels has been developed in recent years to determine the composition of microbial communities. The functional analysis of microbial communities is a unique feature of metaproteomics compared with other omics approaches. Metaproteomics has become an important component of the multi-omics analysis of microbial communities, and has great development potential in terms of depth of coverage, sensitivity of detection, and completeness of data analysis.

Iterative Searching Method for Closest Saddle-Node Bifurcation Point in Multi-Infeed HVDC Systems

Iterative Searching Method for Closest Saddle-Node Bifurcation Point in Multi-Infeed HVDC Systems

How to improve efficiency for prefabricated component production scheduling with worker allocation and order outsourcing?

Prefabricated building is an advanced building mode that is actively promoted in China owing to its high-efficiency, energy-saving and labour-saving characteristics. However, because of the limited production capacity and production resources of the final assembly parts production plant, it is usually impossible for a factory to accept all orders. Therefore, the factory will consider outsourcing part of the orders to reduce delay penalties. In addition, the allocation of workers in the production process will significantly affect the processing time of orders. Regarding those two issues, a production scheduling integrated optimization model, considering worker allocation and order outsourcing, is established to maximize net profit. To address the complexity of problem solving caused by the NP-hard nature of this problem, a hybrid improved genetic–accelerated iterative greedy search algorithm is developed. Finally, a numerical example is delivered to verify that the proposed algorithm has improved solution quality and convergence.

A Multimodal Approach to Measuring Listening Effort: A Systematic Review on the Effects of Auditory Task Demand on Physiological Measures and Their Relationship.

Listening effort involves the mental effort required to perceive an auditory stimulus, for example in noisy environments. Prolonged increased listening effort, for example due to impaired hearing ability, may increase risk of health complications. It is therefore important to identify valid and sensitive measures of listening effort. Physiological measures have been shown to be sensitive to auditory task demand manipulations and are considered to reflect changes in listening effort. Such measures include pupil dilation, alpha power, skin conductance level, and heart rate variability. The aim of the current systematic review was to provide an overview of studies to listening effort that used multiple physiological measures. The two main questions were: (1) what is the effect of changes in auditory task demand on simultaneously acquired physiological measures from various modalities? and (2) what is the relationship between the responses in these physiological measures? Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, relevant articles were sought in PubMed, PsycInfo, and Web of Science and by examining the references of included articles. Search iterations with different combinations of psychophysiological measures were performed in conjunction with listening effort-related search terms. Quality was assessed using the Appraisal Tool for Cross-Sectional Studies. A total of 297 articles were identified from three databases, of which 27 were included. One additional article was identified from reference lists. Of the total 28 included articles, 16 included an analysis regarding the relationship between the physiological measures. The overall quality of the included studies was reasonable. The included studies showed that most of the physiological measures either show no effect to auditory task demand manipulations or a consistent effect in the expected direction. For example, pupil dilation increased, pre-ejection period decreased, and skin conductance level increased with increasing auditory task demand. Most of the relationships between the responses of these physiological measures were nonsignificant or weak. The physiological measures varied in their sensitivity to auditory task demand manipulations. One of the identified knowledge gaps was that the included studies mostly used tasks with high-performance levels, resulting in an underrepresentation of the physiological changes at lower performance levels. This makes it difficult to capture how the physiological responses behave across the full psychometric curve. Our results support the Framework for Understanding Effortful Listening and the need for a multimodal approach to listening effort. We furthermore discuss focus points for future studies.

Protocol for a realist review of case-based learning in undergraduate medical education

Case-based learning has become increasingly adopted as a pedagogical approach in health professions education. The successful implementation of case-based learning programmes can be impacted by factors including resources, organisational attitudes and cultural dynamics. This protocol outlines a methodology for a realist review which aims to elucidate the mechanisms and contexts that influence the implementation of a case-based learning programme in health professions education. This study aims to understand the underlying causal mechanisms and contextual factors which can lead to the success or failure of case-based learning programmes. This review will involve a systematic literature review on case-based learning in health professions education, under a realist synthesis framework. A realist synthesis was chosen as the approach as it is suited to the review of such complex interventions, such as the implementation and delivery of a case-based learning programme. Through an iterative literature search and analysis, relevant literature will be explored in order to develop an understanding of the contextual elements and mechanisms which may affect the success of a case-based learning programme. This review will employ thematic analysis to synthesize findings across studies, iteratively refining a programme theory explaining how case-based learning programmes work, for whom, and in what contexts. This realist review protocol provides a systematic and rigorous approach to examining the complexities of case-based learning and the outcomes produced by its underlying mechanisms and contexts. This review aims to offer valuable insights for educators, curriculum developers, and policymakers to optimize the implementation of case-based learning interventions in medical education.

Deep learning models for predicting the survival of patients with hepatocellular carcinoma based on a surveillance, epidemiology, and end results (SEER) database analysis

Hepatocellular carcinoma (HCC) is a common malignancy with poor survival and requires long-term follow-up. Hence, we collected information on patients with Primary Hepatocellular Carcinoma in the United States from the Surveillance, Epidemiology, and EndResults (SEER) database. We used this information to establish a deep learning with a multilayer neural network (the NMTLR model) for predicting the survival rate of patients with Primary Hepatocellular Carcinoma. HCC patients pathologically diagnosed between January 2011 and December 2015 in the SEER (Surveillance, Epidemiology, and End Results) database of the National Cancer Institute of the United States were selected as study subjects. We utilized two deep learning-based algorithms (DeepSurv and Neural Multi-Task Logistic Regression [NMTLR]) and a machine learning-based algorithm (Random Survival Forest [RSF]) for model training. A multivariable Cox Proportional Hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into a training set and a test set in a 7:3 ratio. The training dataset underwent hyperparameter tuning through 1000 iterations of random search and fivefold cross-validation. Model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-year, 3-year, and 5-year survival rates was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Area Under the Curve (AUC). The primary outcomes were the 1-year, 3-year, and 5-year overall survival rates. Models were developed using DeepSurv, NMTLR, RSF, and Cox Proportional Hazards regression. Model differentiation was evaluated using the C-index, calibration with concordance plots, and risk stratification capability with the log-rank test. The study included 2197 HCC patients, randomly divided into a training cohort (70%, n = 1537) and a testing cohort (30%, n = 660). Clinical characteristics between the two cohorts showed no significant statistical difference (p > 0.05). The deep learning models outperformed both RSF and CoxPH models, with C-indices of 0.735 (NMTLR) and 0.731 (DeepSurv) in the test dataset. The NMTLR model demonstrated enhanced accuracy and well-calibrated survival estimates, achieving an Area Under the Curve (AUC) of 0.824 for 1-year survival predictions, 0.813 for 3-year, and 0.803 for 5-year survival rates. This model's superior calibration and discriminative ability enhance its utility for clinical prognostication in Primary Hepatocellular Carcinoma. We deployed the NMTLR model as a web application for clinical practice. The NMTLR model have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with primary liver cancer.

A Study on An MPPT Control Approach Using Artificial Intelligence and the Perturb and Observe Method

A maximum power point tracking (MPPT) control procedure constructed based on the Artificial intelligence optimization algorithm is proposed. A mathematical model of photovoltaic cells was established under varying light intensity and ambient temperature. Maximal power tracking control for iterative search using an artificial intelligence (AI) optimization technique Excellent, the artificial intelligence-based MPPT algorithm's principle is presented. Simulation results it shows that the artificial intelligence algorithm can faster and exactly track the maximum power point and remains stable, and compared to Perturb and Observe algorithm under dynamic shadow conditions and artificial intelligence MPPT control method, which has higher tracking accuracy and faster convergence speed. Faster and smaller oscillation amplitude, which is the best response to sunlight conditions for photovoltaic maximum power point tracking technology Flexibility. Using Matlab/Simulink software to build a simulation model of an independent photovoltaic system. It controls variables by keeping the temperature continuous and changes the light intensity to simulate different lighting environments. The identical comparison was conducted between all based MPPT methods such as the fuzzy control approach, demonstrating that the fuzzy control based technique exhibits higher results in terms of efficiency.

Impact load identification method based on frequency response pattern recognition and dynamic sensor filter strategy

Identification of impact loads plays important role in marine structures health monitoring but is difficult to be measured directly most time. This study investigates a two-stage framework for impact load localization and reconstruction, consisting of load region identification and local refined nodal search. For the region identification, a novel frequency response feature preprocessing method based on FFT is proposed and incorporated into a multi-layer perceptron (MLP) neural network as the embedding function of the Matching Network (MN), the core model adopted for pattern recognition. Based on the region probabilities predicted by MN, a local refined nodal search strategy is provided, which is initialized by a region correction method for amending the possible region misclassification and further guided by error metrics with iteration search strategy. Moreover, the inverse problem in this study is formulated in the discretized state space expression with the reduced modal coordinates. For improving the load inverse accuracy affected by Zero Order Hold (ZOH) simplification in this formulation, a dynamic sensor filter strategy is provided. Eventually, a numerical experiment of impact load identification on a steel plate is performed and discussed, whose results indicate the validity and robustness of the proposed method.

Implementation of inverse method for identification of process parameters in a laser heating process

Changes in process parameters can alter the temperature data of a solid body during the laser heating process. This paper proposes an inverse method to determine the laser heating parameters, namely, sheet width, sheet thickness, laser power, and scan speed of the work specimen for the given temperature response which is assigned by the user. The method uses an analytical tool on moving heat source as a forward model capable of real-time temperature prediction of the sheet under the laser heating process. The effectiveness of the present forward model that incorporated the temperature-dependent material properties is tested by experimental findings performed on Al 6061-T6 sheets. Next, an iterative search process based on heuristic method is carried out for satisfying a prescribed temperature response by the optimization of unknown parameters. To illustrate the implementation and assess the practicality of the inverse method, two examples based on laser heating applications are used. The method proposed herein recover the unknowns for the prescribed temperature response after a few iterations, provides an efficient way to optimize the laser heating process. Additionally, the effect of measurement error on the findings of the inverse problem is addressed.

Improved dragonfly optimization algorithm based on quantum behavior for multi-objective optimization of ethylene cracking furnace

Ethylene is one of the important basic raw materials for the modern chemical production, accounting for over 75 % of petrochemical products. And the ethylene cracking furnace is subject to coking problems that can impede the thermal conductivity of the reaction tube. Therefore, an improved multi-objective dragonfly optimization algorithm based on quantum behavior is proposed. The particle evolutionary model introduces the quantum bound state δ-potential well model, which improves the global convergence ability and the convergence speed of the dragonfly optimization algorithm. In addition, the calculation formulas for parameters are improved by introducing natural logarithm and the iterative search method is changed to a triple search. Moreover, the global convergence and local search ability of the quantum behavior enhancement algorithm is assisted by adaptively selecting the search strategy according to the current number of iterations of the improved dragonfly algorithm. Compared with other multi-objective optimization algorithms on the ZDT and CEC test functions, the proposed algorithm has good global convergence and local search capabilities in terms of the inverted general distance. Finally, the proposed algorithm is applied in the multi-objective optimization of an ethylene cracking furnace, with the ethylene yield and the propylene yield as optimization objectives. The experimental results show that the ethylene yield can be increased by 1.5498 %, while the propylene yield decreases by only 0.0081 %, which proves that the proposed algorithm achieves better effect in optimizing the main product yield under fixed cycle adjustment operating conditions compared to original operating conditions.

Fault Distance Measurement Method Based on Wavelet Energy Spectrum and BWO Algorithm Optimized CNN-GRU Hybrid Neural Network

Aiming at the problems such as noise interference in the current fault ranging methods for flexible DC transmission lines, a single-ended intelligent fault ranging method is proposed as a hybrid neural network based on wavelet energy spectrum and BWO algorithm to optimize the multi-head attention mechanism of CNN combined with gated recurrent unit GRU. First, the correlation between wavelet spectrum energy and fault distance is analyzed, and wavelet packet decomposition is used to extract the wavelet packet energy spectrum feature vector as the model input of the neural network. Second, the hybrid neural network model is constructed and trained to mine the deep fault information in the time series, and the parameters of the hybrid neural network model with added multi-head attention are optimized using the iterative optimization search of the beluga algorithm to achieve fast and accurate positioning of the fault distance. Finally, the network is trained using the constructed four-terminal Zhangbei flexible DC transmission system model to collect data, and the experimental results prove that the method has high distance measurement accuracy, strong anti-interference ability and generalization ability, and a certain degree of resistance to transition resistance.

The (dis)organization of leg ulcer care: A realist synthesis.

Venous leg ulcers affect 1.5% of the UK adult population. Leg ulcers are painful, can be malodourous and are associated with poor quality of life. Leg ulcers are predominantly cared for by nurses in the community. Frequently, patients receive suboptimal treatment through unwanted variations in care and simple ulcers deteriorate to become hard-to-heal wounds. It is important to understand the current UK system of care and how nurses and patients navigate through it. The aim of this paper was to understand how, when, for whom and in what context leg ulcers are cared for in the United Kingdom and specifically, the current system of care, the nurses' role and the patients' experience in this system of care. A realist synthesis of the literature was undertaken, reported following the RAMESES publication standards: Realist syntheses. An iterative literature search was conducted across three recognized health collections from January 2010 to January 2022 that included descriptive studies as well as primary research. 73 papers were included. In the absence of UK national guidance that recommends how leg ulcer care is organized and delivered, care is commissioned locally, with variable outcomes. Patients with venous leg ulcers would like to be looked after by knowledgeable, skilled and confident nurses, in well-equipped and staffed clinics; nurses who have the ability to make clinical judgements to alter their treatment when necessary and are empowered to refer to specialist centres when further support is required. This synthesis offers guidance to commissioners and providers to change how leg ulcer care is organized. The views of a patient and public group was sought at each stage of the synthesis.

Effective Assistance for Iterative Visual Search Tasks by Using Gaze-Based Visual Cognition Estimation

Effective Assistance for Iterative Visual Search Tasks by Using Gaze-Based Visual Cognition Estimation

Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators

Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators

Low‐Frequency Earthquakes Downdip of Deep Slow Slip Beneath the North Island of New Zealand

AbstractWe report the first catalog of low‐frequency earthquakes in the Hikurangi subduction zone, located beneath the Kaimanawa Range of the North Island at 50 km depth, downdip of regularly recurring (every 4–5 years) deep M7 slow slip events. To systematically detect low‐frequency earthquakes within the regional continuous seismic data, we utilized a matched‐filter approach with template waveforms derived from previous observations of tectonic tremor. We built our catalog of 36 low‐frequency earthquake sources, that produced almost 21,000 events over more than a decade, with two matched‐filter search iterations. In each iteration, the detections were gathered into families and their coherent waveforms processed and stacked to extract high‐quality waveforms, allowing us to pick seismic phase arrivals to locate the low‐frequency earthquakes. We highlight three characteristic features to validate that our detected events are indeed low‐frequency earthquakes: the eponymous deficit of high frequencies in their seismic waveforms, the episodic swarms of activity that define their activity through time, and their location at the plate boundary with a double‐couple source mechanism and geometry consistent with the subduction interface. Considering the observed low‐frequency earthquakes' relationship to neighboring slow slip, we observe the event swarms to occur much more frequently than the M7 slow slip events located just updip. Similar to other deep low‐frequency earthquakes in other subduction zones, we suggest that this characteristic clustering in time is driven by more frequent, smaller slow slip events that are not clearly observable at the surface.

Terminal Trajectory Planning for Synthetic Aperture Radar Imaging Guidance Based on Chronological Iterative Search Framework.

Synthetic aperture radar (SAR) is capable of obtaining the high-resolution 2-D image of the interested target scene, which enables advanced remote sensing and military applications, such as missile terminal guidance. In this article, the terminal trajectory planning for SAR imaging guidance is first investigated. It is found that the guidance performance of an attack platform is determined by the adopted terminal trajectory. Therefore, the aim of the terminal trajectory planning is to generate a set of feasible flight paths to guide the attack platform toward the target and meanwhile obtain the optimized SAR imaging performance for enhanced guidance precision. The trajectory planning is then modeled as a constrained multiobjective optimization problem given a high-dimensional search space, where the trajectory control and SAR imaging performance are comprehensively considered. By utilizing the temporal-order-dependent property of the trajectory planning problem, a chronological iterative search framework (CISF) is proposed. The problem is decomposed into a series of subproblems, where the search space, objective functions, and constraints are reformulated in chronological order. The difficulty of solving the trajectory planning problem is thus significantly alleviated. Then, the search strategy of CISF is devised to solve the subproblems successively. The optimization results of the preceding subproblem can be utilized as the initial input of the subsequent subproblems to enhance the convergence and search performance. Finally, a trajectory planning method is put forward based on CISF. Experimental studies demonstrate the effectiveness and superiority of the proposed CISF compared with the state-of-the-art multiobjective evolutionary methods. The proposed trajectory planning method can generate a set of feasible terminal trajectories with optimized mission performance.

High Alert Medication Mitigation Strategies (HAMMS): A rapid realist review for assessing what works, how, and in what circumstances in a full service Irish acute hospital

Abstract Introduction All medications have the potential to cause harm; however, some are prone to cause more serious harm than others - titled “high-alert medications”. High-alert medications are identified from national/international consensus and from local patient incidents. Safeguarding of high-alert medications through mitigation strategies is complex and can represent a multi-faceted approach; international consensus, public guidance and local data inform interventions to reduce the risk of error. Realism can explore complex interventions, whereby rapid realist reviews are used when the evidence is required for a local intervention, and the results are not required to be relevant in other situations. Aim This rapid realist review aimed to present an evidence-informed approach of high-alert medication mitigation strategies within an acute hospital, how they work, why and the specific circumstances they work in. Methods Realist methodologies explores the context (C) of strategies/interventions (I) and the mechanisms (M) by which they operate to impact outcomes (O), which in this case is to reduce the risk of errors associated with high-alert medications. Audit data and stakeholder discussion informed the development of an initial programme theory. A rapid literature review across four databases was then conducted to test the initial programme theory, where evidence of how interventions work to reduce errors in specific contexts was extracted using Microsoft Excel®. Papers from initial rapid reviews and iterative searches were assessed by relevance and rigour through NVivo12® software, until theoretical saturation of the programme theory was reached, and the development of a modified programme theory occurred. Results Thirty-two papers were included in the final synthesis. Specific to the institution, employing redundancies, standardisation, warning labels and automation were the most commonly applicable interventions related to mitigating the risk of high-alert medications. Governance, confidence, limiting access and engagement, among other adaptive and responsive mechanisms are needed to facilitate these interventions. Influence is spread across organisational, ward and individual perspectives for increasing access to information. Based on audited data, insulin medication management represented a specific area for focus; therefore, a list of ten recommendations were drawn up using guiding principles from realist findings. Conclusion A suite of high- to low-leverage strategies related to high-alert medications, applied within specific contexts, could be adopted within the institutions current infrastructure. Further research is needed to evaluate the adoption of the strategies to determine their success and validate the data. Rapid realist reviews are useful methods in capturing complex interventions within specific institutions in a time-sensitive manner.