Throughput Model Research Articles

ATP: Achieving Throughput Peak for DNN Training via Smart GPU Memory Management

Due to the limited GPU memory, the performance of large DNNs training is constrained by the unscalable batch size. Existing researches partially address the issue of GPU memory limit through tensor recomputation and swapping, but overlook the exploration of optimal performance. In response, we propose ATP, a recomputation and swapping based GPU memory management framework that aims to maximize training performance by breaking GPU memory constraints. ATP utilizes a throughput model we proposed to evaluate the theoretical peak performance achievable by DNN training on GPU, and provide the optimum memory size required for recomputation and swapping. We optimize the mechanisms for GPU memory pool and CUDA stream control, employs an optimization method to search for specific tensors requiring recomputation and swapping, thereby bringing the actual DNN training performance on ATP closer to theoretical values. Evaluations with different types of large DNN models indicate that ATP achieve throughput improvements ranging from 1.14 ∼ 1.49 ×, while support model training exceeding the GPU memory limit by up to 9.2 ×.

Creep Lifetime Prediction of Alloy 617 Using Black Box Machine Learning Approach

Abstract This study explored the application of black box machine learning (ML) to build high throughput models that predict the creep response of Ni-based Alloy 617. Black box ML refers to highly complex machine learning algorithms that generate outputs from inputs without an interpretable internal process. The rapid implementation of suitable heat of alloys into targeted service is impeded by the extended qualification process involving chemistry-processing-structure-performance assessment. The ASME B&PV Code III outlines the requirement of 10,000 h of creep testing before each heat can be qualified for service and 30,000 h for heats that exhibit metastable phases. There is a critical need to shorten the development-to-deployment timeline for heats of an alloy at specific applications. Recent advancement in ML offers the ability to identify correlations in data which is difficult to elucidate by other approaches. To that end, black box ML is employed to expedite the HEAT qualification of Alloy 617 and predict performance from HEAT chemistry out to an unprecedented timescale. In this study, creep data for Ni-based Alloy 617—a solid solution strengthened material is gathered from a wide range of data sources. The alloy chemistry, phases, precipitates, and microstructural features are analyzed to obtain the key strengthening mechanism. Service conditions, mechanical properties, chemistry, and chemical ratios are provided as potential input features. The Pearson correlation coefficient with a 95% confidence bound is employed for input feature screening where poorly correlated inputs are eliminated to speed up the ML process and prevent under- and/or over-fitting of predictions. In the ML algorithm, the selected input features are regarded as predictors, and rupture is regarded as the response. An algorithm evaluation is performed where 20 ML algorithms are trained with the training set. The three-layered neural network (NN) was observed to be the best algorithm for the given data based on statistical rationale. The NN accurately predicts rupture across a range of isotherms and data sources. The interpolative and extrapolative predictions are in compliance with ECCC V5 guidelines. A post-audit validation exhibits neither under- nor over-fitting and confirms the applicability of NN algorithms to unseen data. The black box ML provides a pathway to predict the performance directly from chemistry and opens avenues to rapid heat qualification.

APPLICATION OF IN VITRO MODELS: INCORPORATING BLOOD BRAIN BARRIER MODELS WITH GLIOMA, AND THE USE OF MICROflUIDICS

Abstract AIMS The blood brain barrier (BBB) presents one of the main obstacles for the development of novel glioblastoma treatments. The various transporters and enzymes within the BBB reduce the disposition of novel therapies into the brain. There is a need for an in vitro BBB-glioma model with proven efficacy of predicting CNS delivery at earlier stages in the drug discovery pipeline. METHOD A BBB-glioma lab-on-a-chip model could enable rapid identification of novel therapies that can cross the BBB at earlier stages of the drug development process, before going to clinical trials. The application of microfluidic technology allows for the detection of soluble tissue-derived factors and the assessment of the levels of soluble markers released from malignant tissue. Adding ‘flow’ to the model, is more reflective of the dynamic flow present at the BBB. RESULTS Validation studies of the current BBB model and converting this model over to a lab-on-a-chip model have shown the presence of an endothelial cell barrier and the tight junction proteins Zo-1 and Claudin-1, through western blotting, immunocytochemistry, and permeability experiments using varying molecular weights of FitC-dextran. These validation studies have been completed using brain endothelial monolayers on polycarbonate transwell inserts, with the immortalised cell line HCMEC/d3, and the short-term culture cells HBMEC. Also, validation studies using a co-culture of endothelial cells, astrocytes and pericytes have begun, using the same techniques to show a barrier has formed, on both the current BBB model and the lab-on-a-chip model. CONCLUSION The aim of this study is to combine the use of an all-human BBB-glioma model with lab-on-a-chip technology to produce a high throughput model to test novel therapies at an earlier stage. The lab-on-a-chip model will be validated for the purpose of measuring permeability and drug response and to investigate the expression of novel targets verses expression in conventional models.

Lean-Based Approach to Improve Emergency Department Throughput.

Objective:Lean methodology can be utilized to increase throughput in a fast-track care area without changing staffing in a busy, urban emergency department (ED). Methods: A retrospective before-and-after analysis was performed to an improvement process in a fast-track care area within an ED with a census of 100,000 patients. The intervention utilized Lean methodologies to identify inefficiencies in the throughput model set for patients triaged to fast-track. Multiple ED stakeholders were involved in formulating a more efficient framework for how patients would receive care when triaged to fast-track. Results: There was a decline in the patient's overall length of stay (-9%, p=0.08), arrival to the room (-10%, p=0.4418), and ED attending to disposition (-9%, p=0.003). Additionally, all aspects of patients leaving prior to treatment completion (against medical advice (-29.4%, p=0.006), elopement (-20.4%, p=0.049), and left without being seen (-5.3%, p=0.11)) declined. Discussion:Identifying wasted time and resources in a patient's stay in the ED allowed for a more efficient throughput model to be developed. This resulted in patients being able to be seen in a more methodical manner leading to decreased wait times and lower left without being seen rates. Conclusion: A Lean-based throughput model was implemented to improve efficiency, reducing the length of stay and increasing the volume of patients evaluated per shift without additional costs. This improvement led to fewer patients leaving before treatment and demonstrated the value of process improvement in healthcare.

Fit-for-Purpose Model of HP500 Cone Crusher in Size Reduction of Itabirite Iron Ore

Cone crushers have a central role in the processing of quarry rocks, besides coarser ore preparation in several mineral processing plants. This is particularly true in the case of Itabirite iron ore preparation plants in Brazil, so optimizing their performance is of central importance for reaching maximum productivity of the circuit. The work presents results of modeling the HP500 cone crusher in operation in an industrial plant in Brazil (Minas Rio), from surveys carried out over a few years with different feeds and crushing conditions. A version of the Andersen–Whiten cone crusher model was implemented in the Integrated Extraction Simulator featuring a non-normalizable breakage response and a fit-for-purpose throughput model. The results demonstrate the good ability of the model to predict crusher performance when dealing with different closed-side settings and feed size distributions.

A robust bi‐level programming approach for optimal planning an off‐grid zero‐energy complex

AbstractElectric power provision for all the customers is not always possible for distribution companies. Some customers are interested in serving their load through renewable resources regarding the climate situation. Electrically off‐grid zero‐energy building is an applicable concept, in which the electrical energy provision of the buildings is isolated from the power supply infrastructures. This paper introduces a robust bi‐level programming model to create a cost‐effective off‐grid zero energy complex in Kish Island under risk management. The upper level of the planning is composed of two components, the passive design of buildings within the complex and the design of a stand‐alone energy system. The passive design as an energy‐saving tool includes the selection of insulation material for building external walls and finding the optimum thickness. Also, the stand‐alone energy system design denotes the sizing of diesel generator, photovoltaic, and battery energy storage as the distributed energy resources. The lower‐level problem optimally handles the annual scheduling of these resources to meet the complex demand under the impact of passive cooling. The Karush–Kuhn–Tucker condition method is used to solve this bi‐level planning problem. Furthermore, the battery degradation is concerned via the throughput model to consider the replacement cost of the problem.

How can a community be successfully empowered to deal with death, dying, and bereavement?-formative evaluation of the Caring Community Cologne using focus groups.

Compassionate communities aim to empower people to deal with death, dying, and bereavement. They also intend to facilitate access to care and support at the end of life. However, there is a need for systematic knowledge on how to achieve the desired outcomes for citizens and for insights regarding the development, implementation, and evaluation. The aim of this study was to assess the views of members of a German Compassionate City, the "Caring Community Cologne" (CCC), and to report on its practical implementation. The CCC consists of a citywide Round Table, a Steering Group, a Coordination Office and four Working Groups in areas where activities are already in place. We conducted two qualitative focus groups with nine members of three Working Groups. The transcripts were analysed with qualitative content analysis, using MAXQDA version 2022, and results were transferred into the logic model "Throughput Model". At the time of evaluation, participants felt that the structures of the CCC were adequate, but criticised the cooperation and transparency between them. A key aspect of this was the requirement for a coordinating body. They stressed the support of federal institutions as a key factor, while at the same time describing insufficient citizen involvement. The transfer of the results into the Throughput Model highlighted four areas that the CCC should address: (I) neighbourhood networks need to be established to strengthen civic support; (II) people need to be made aware of the issues by making them accessible in their everyday lives; (III) the many existing support initiatives need to be better linked and made more accessible; (IV) adequate healthcare service structures have to be guaranteed. The top-down approach described, supported by the city's engagement and involving existing initiatives can facilitate the development of a bottom-up civic engagement model in a large city. However, active citizen involvement appeared to be a challenge. The Throughput Model was a suitable basis for mapping work processes and developing evaluation plans.

New recellularizable cardio-matrix scaffold as a base for alternative in vitro models for the cardiotoxicity risk prediction

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): SID 227944/22 DOR 2331157/23 Background Cardiotoxicity is the occurrence of cardiac dysfunction induced by pathophysiological stimuli as a result of toxic effects. A considerable number of drugs, including chemotherapics, antibiotics antidepressants but also antiarrhythmics, could cause an alteration of cardiac physiology, and then cardiac damage. These include disturbances in ventricular repolarization and QT interval, arrhythmias, bradycardia, tachycardia, decreases in left ventricular ejection fraction, and congestive heart failure. Precinical evaluation of drug cardiotoxicity has used animal models, which tend to be expensive, have lo throughput, and have limitations as not ever faithfully reflect the human pathophysiology, while current two-dimensional cellular models revealed to be not sufficient and specific. Purpose The aim of this study is to develop a myocardial tissue-like scaffold, in order to realize an efficient and easy-replicable in vitro model for drug cardiotoxicity prediction. This must be able to mimic the morphological, cellular, and electrophysiological complexity of the intact adult human myocardium. Material and Methods Starting from fresh porcine hearts, cardiac specimens were isolated from left ventricles using a scalpel, sectioned with a vibratome and punched with a biopsy puncher obtaining myocardial patches. Thus, samples were decellularized through a novel serial decellularization treatment based on osmotic shock and detergents. The decellularization procedure exploited a new automated, dynamic perfusion device for a standardized and optimized removal of cardiac cells and non-ECM proteins. These samples were analysed for decellularization effectiveness by DNA quantification, histology and immunofluorescence staining to evaluate cell removal and extracellular matrix integrity. Moreover, they were tested for cytocompatibility using human mesenchymal stem cells and cardiac progenitors, also derived from induced pluripotent stem cells. Cellular adhesion, behaviour, vitality, proliferation, and possible apoptotic effects were tested, too. Results In order to advance a robust bioelectronics model, an automated platform was developed to generate a three-dimensional, bioengineered replica of human myocardial tissue. With respect to the common decellularization protocol characterized by mild agitation, results based on the automated, dynamic treatments have shown a superior ability to obtain acellular, biocompatible scaffolds in terms of cell elements’ removal and extracellular matrix preservation, as well as time- and cost-effectiveness. Indeed, these decellularized scaffolds allow cell penetration, adhesion and survival. Conclusions Further experiments will be focused on evaluating functional bioengineered myocardial tissues with electrophysiological and molecular analysis to evaluate this new model's throughput capacity. Finally, the validation of these bioelectronic platforms will be performed with drugs with known cardiotoxic effects.

Forecast combination using grey relational analysis and Choquet fuzzy integral for container throughput forecasting

Utilizing the accurate and reliable demand forecasting model for the port logistics industry decision makers, can prevent misguided decisions ensuing from inaccurate forecasting. Current forecasting models of container throughput forecasting focus on the development of single or related hybrid models. However, these studies focus on improving the forecast accuracy, while neglecting the risk of failure. Combination forecasting can improve the forecast accuracy and reduce the risks from model selection failure. The previous research on port container throughput (PCT) combination forecasting focused on using a small number of linear models to generate the combined values, and neglected the nonlinear combined models reflecting the interaction between variables. Given the significance of combination forecasting for the PCT prediction, this study establishes a nonlinear combined model using the grey relational analysis (GRA) and Choquet fuzzy integral. The empirical results indicate that the proposed combined model outperforms both the other considered linear and nonlinear models. The findings assist the port decision-makers in both the public and private sectors for understanding the future PCT, and devising timely response plans aligned with actual circumstances and trends, besides formulating the appropriate development plans and decisions to modify the current situation or sustain competitive advantages.

Performance of hybrid transmission in a reconfigurable intelligent surface assisted D2D using NOMA

Recent research on Reconfigurable Intelligent Surface (RIS) aided communication and Non-Orthogonal Multiple Access (NOMA) technology shows enhanced spectral efficiency for 5 G and beyond wireless network systems. This paper evaluates the performance of a Device-to-Device (D2D) network where RIS is deployed in a suitable position and NOMA is employed to accommodate more number of users in the spectrum simultaneously. The D2D users exploit cellular network resources. Analytical models for outage, throughput, and energy-efficiency are developed considering spectrum sensing and a transmit power constraint. This work investigates the network performance in underlay, overlay, as well as hybrid modes through extensive simulations. The simulation model is designed based on the analytical models. Outage probability, network throughput, and energy efficiency are analysed to evaluate the D2D network performance. The simulation results shows incorporation of RIS and NOMA, network performance improves substantially. Increase in number of RIS elements also improves the network performance.

Evaluating Reception Center Models for Radiation Response Screening Capacity and Throughput Predictions.

Introduction: The current fleet of nuclear reactors in the United States is mandated to provide evidence that surrounding jurisdictions can screen their populations should an incident occur. Capacity can be measured as throughput in reception centers used for screening. Due to the significant staffing and resources required to exercise screening capacity, most jurisdictions typically perform smaller exercises and use models to estimate their overall throughput. Objective: To evaluate the applicability and realism of current throughput models and practices. Methods: Throughput capacity for radiation screening is estimated with a mathematical model derived by the Federal Emergency Management Agency (FEMA). The Centers for Disease Control and Prevention developed a discrete event simulation model as a tool, SimPLER, to evaluate capacity and make throughput predictions. Model estimates will be compared and evaluated using timing data collected at a large-scale exercise. Results: The FEMA model estimated a throughput 41.2% higher than the actual radiation screening throughput, while the SimPLER model provided identical values. The FEMA and SimPLER models' predicted throughputs were 50% and 3.8%, respectively, higher than total exercise throughput. Applying each model to the throughput projections for a 12-hour shift, the FEMA model estimates ranged from 665 to 6,646 people and the SimPLER model yielded an estimated throughput of 1,809 people with a standard deviation of 74.6. Conclusion: Discrete event simulation models, such as SimPLER, may provide more realistic and accurate predictions of radiation screening and throughput capacity of reception centers than mathematical models such as the FEMA model.

Systemic Modeling and Prediction of Port Container Throughput Using Hybrid Link Analysis in Complex Networks

This paper introduces a hybrid framework for port container throughput forecasting, which is essential in global trade and transportation systems. It uses a multidisciplinary method that combines artificial intelligence, link prediction, and complex networks. To better grasp the interconnection and dynamics of port operations, time series data are first transformed using complex network theory into a network structure. The framework applies 13 similarity metrics, encompassing various aspects of network structural similarity, to form a feature set representing the complex port operation network. The most effective features are selected using the maximum relevance minimum redundancy (mRMR) method, adhering to systems theory’s efficiency principles. These features are processed through SVM, DNN, and LSTM models for link prediction, which is crucial for forecasting in port logistics. Finally, the methodology concludes with regression analysis to obtain container throughput forecasts, which is a key metric in port systems management. Case studies of Shanghai Port and Shenzhen Port validate the framework’s effectiveness, demonstrating a significant improvement in forecasting accuracy over the baseline models. This study contributes to systems analysis by showcasing a hybrid, AI-enhanced approach for managing and forecasting critical aspects of maritime trade systems.

Mathematical Modeling and Statistical Analysis of Efficient Cluster Head Selection Based LEACH Protocol for Wireless Sensor Networks

The collection of data from the physical environment, such as temperature and humidity readings, is an essential function of Wireless Sensor Networks (WSNs). Because of its ability to communicate data wirelessly, WSNs are crucial for connecting the digital and physical worlds. Nevertheless, WSN energy efficiency optimization has become a more complicated challenge because of their battery dependency and deployment in adverse locations. The emphasis of this research is the Improved Low-Energy Adaptive Clustering Hierarchy (ILEACH) protocol, which is used as an example of a mathematics-driven strategy to improve WSN energy efficiency. To tackle important problems with energy consumption and network lifetime, mathematical modelling and equation-based analysis are used to study and enhance ILEACH's performance. Particularly in faraway places with few resources, energy efficiency is of the utmost importance when discussing WSNs. This study's mathematical models explore the equations and probability distributions that control the lifetime, energy consumption, and behaviour of sensor nodes. Our goal is to shed light on the energy optimization processes of ILEACH by means of thorough mathematical analysis and derivations. Our method incorporates both theoretical modelling and empirical assessments of ILEACH's cluster head formation, throughput, and network performance as a whole. Our comparison of the mathematical formulations of ILEACH and LEACH shows that our Improved LEACH is better in terms of data throughput, network lifetime, and energy efficiency. Using a figure of merits criterion, we compare LEACH, ILEACH, and the proposed Improved LEACH in detail to help with decision-making and protocol selection. With its solid mathematical groundwork, this study is a great tool for improving the efficiency of WSNs' energy-saving algorithms, which in turn can help networks last longer and run better.

Throughput Modeling and Validation of MIMO Terminals With Adaptive MCS and Layers

Throughput Modeling and Validation of MIMO Terminals With Adaptive MCS and Layers

A Differentiable Throughput Model for Load-Aware Cellular Network Optimization Through Gradient Descent

A Differentiable Throughput Model for Load-Aware Cellular Network Optimization Through Gradient Descent

In Vitro Models for Investigating Intestinal Host-Pathogen Interactions.

Infectious diseases are increasingly recognized as a major threat worldwide due to the rise of antimicrobial resistance and the emergence of novel pathogens. In vitro models that can adequately mimic in vivo gastrointestinal physiology are in high demand to elucidate mechanisms behind pathogen infectivity, and to aid the design of effective preventive and therapeutic interventions. There exists a trade-off between simple and high throughput models and those that are more complex and physiologically relevant. The complexity of the model used shall be guided by the biological question to be addressed. This review provides an overview of the structure and function of the intestine and the models that are developed to emulate this. Conventional models are discussed in addition to emerging models which employ engineering principles to equip them with necessary advanced monitoring capabilities for intestinal host-pathogen interrogation. Limitations of current models and future perspectives on the field are presented.

Biodosimetry Based on Gamma-H2AX Quantification in Human Peripheral Blood Lymphocytes after Partial-body Irradiation.

Quantification of gamma-H2AX foci can estimate exposure to ionizing radiation. Most nuclear and radiation accidents are partial-body irradiation, and the doses estimated using the total-body irradiation dose estimation formula are often lower than the actual dose. To evaluate the dose-response relation of gamma-H2AX foci in human peripheral blood lymphocytes after partial-body irradiation and establish a simple and high throughput model to estimate partial-body irradiation dose, we collected human peripheral blood and irradiated with 0-, 0.5-, 1-, 2-, 3-, 4-, 5-, 6-, and 8-Gy gamma rays to simulate total-body irradiation in vitro. Gamma-H2AX foci were quantitated by flow cytometry at 1 h after irradiation, and a dose-response curve was established for total-body irradiation dose estimation. Then, a partial-body irradiation dose-response calibration curve was established by adding calibration coefficients based on the Dolphin method. To reflect the data distribution of all doses more realistically, the partial-body irradiation dose-response calibration curve was divided into two sections. In addition, partial-body irradiation was simulated in vitro, and the PBI data were substituted into curves to verify the accuracy of the two partial-body irradiation calibration curves. Results showed that the dose estimation variations were all less than 30% except the 25% partial-body irradiation group at 1 Gy, and the partial-body irradiation calibration dose-response curves were YF 1 = - 3.444 x 2 + 18.532 x + 3.109, R 2 = 0.92 (YF ≤ 27.95); YF 2 = - 2.704 x 2 + 37.97 x - 56.45, R 2 = 0.86 (YF > 27.95). Results also suggested that the partial-body irradiation dose-response calibration curve based on the gamma-H2AX foci quantification in human peripheral blood lymphocytes is a simple and high throughput model to assess partial-body irradiation dose.

Co-Culture Device for in vitro High Throughput Analysis of Cancer-Associated Fibroblast and Cancer Cell Interactions

Introduction: Cancers in general, and specifically lung cancer, continue to have low patient survival rates when the patient is at an advanced stage when diagnosed. It appears that the local environment, especially fibroblasts and their signaling molecules, tends to induce metastasis, increase cancer cell resistance to treatment, and aid in tumor growth rates. Since 3-D models quickly become too complex and/or expensive and therefore rarely leave the lab they are developed in, it is interesting to develop a 2-D model that more closely mimics clustered tumor formation and bulk interaction with a surrounding fibroblast environment. Methods: In the present study, we utilize an off-the-shelf stereolithography 3-D printer, standard use well plates, magnets, and metallic tubes to create a customizable 2-D co-culture system capable of being analyzed quantitatively with staining and qualitatively with standard fluorescent/brightfield microscopy to determine cancer-fibroblast interactions while also being able to test chemotherapeutic drugs in a high-throughput manner with standard 96-well plates. Results: Comparisons from monoculture and co-culture growth rates show that the presence of fibroblasts allows for significantly increased growth rates for H460 cancer. Additionally, the viability of cancer cells can be quantified with simple cell staining methods, and morphology and cell-cell interactions can be observed and studied. Discussion: The high throughput model demonstrates that boundary condition changes can be observed between cancer cells and fibroblasts based upon the different chemotherapeutics that have been administered.

A comparative study of prediction methods for semi-autogenous grinding mill throughput

The mining industry is experiencing a growing amount of stored production data, yet the full potential of these datasets in process modelling remains unexplored. Semi-autogenous grinding (SAG) mills are extensively used in the grinding circuit of mining plants. Precise prediction of SAG mill throughput can result in significant economic benefits, as it can be utilized for better parameter settings to achieve higher throughputs. Furthermore, the development of an accurate throughput prediction model can assist in informed decision-making for long-term planning. The model’s ability to reveal the overall effect of various inputs and estimate the potential throughput change associated with altering each input, can be helpful for determining whether to invest in altering inputs that are laborious and expensive. Numerous SAG mill models have been investigated in the literature; however, a few studies were aimed at forecasting mill throughput. In this research the most accurate prediction model for SAG mill throughput will be investigated through comparing six machine learning models, including genetic programming, recurrent neural networks, support vector regression, regression trees, random forest regression, and linear regression. To achieve this purpose, a real-world data set comprised of 20,161 records from a gold mining complex in Western Australia is investigated and the effective parameters are identified as SAG mill turning speed, power draw of SAG mill, inlet water, and input particle size. As the data set is in the form of time series, the time-dependent nature of the data is considered for prepossessing, model selection, and final comparison. Specially for the first time in this research, delays in data are investigated and utilized to improve prediction performance. Moreover, hyperparameter tuning is performed to determine the best parameter setting for each model prior to implementation. The comparison results demonstrate that the recurrent neural network is the most accurate prediction model, followed by genetic programming and support vector regression. The genetic programming approach is also able to provide a mathematical equation for the SAG mill throughput prediction, which is highly valued by experts in the industry. Sensitivity analysis revealed that the two factors that most significantly affect SAG mill throughput are turning speed and inlet water. It is anticipated that the SAG mill throughput will rise as the SAG mill turning speed increases and the input water decreases.

Zebrafish as a high throughput model for organ preservation and transplantation research.

Despite decades of effort, the preservation of complex organs for transplantation remains a significant barrier that exacerbates the organ shortage crisis. Progress in organ preservation research is significantly hindered by suboptimal research tools that force investigators to sacrifice translatability over throughput. For instance, simple model systems, such as single cell monolayers or co-cultures, lack native tissue structure and functional assessment, while mammalian whole organs are complex systems with confounding variables not compatible with high-throughput experimentation. In response, diverse fields and industries have bridged this experimental gap through the development of rich and robust resources for the use of zebrafish as a model organism. Through this study, we aim to demonstrate the value zebrafish pose for the fields of solid organ preservation and transplantation, especially with respect to experimental transplantation efforts. A wide array of methods were customized and validated for preservation-specific experimentation utilizing zebrafish, including the development of assays at multiple developmental stages (larvae and adult), methods for loading and unloading preservation agents, and the development of viability scores to quantify functional outcomes. Using this platform, the largest and most comprehensive screen of cryoprotectant agents (CPAs) was performed to determine their toxicity and efficiency at preserving complex organ systems using a high subzero approach called partial freezing (i.e., storage in the frozen state at -10°C). As a result, adult zebrafish cardiac function was successfully preserved after 5 days of partial freezing storage. In combination, the methods and techniques developed have the potential to drive and accelerate research in the fields of solid organ preservation and transplantation.