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Manufacturing EngineeringTop 10 Best Mechanism Design Software of 2026
Top 10 Mechanism Design Software ranked for technical buyers, with tool comparisons covering Pyomo, Arena Simulation, and Wolfram Cloud.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Pyomo
Model-to-data separation using sets and parameters for parameterized mechanism experiments.
Built for fits when research and engineering teams automate mechanism model generation via code and structured scenario data..
Arena Simulation
Editor pickScenario provisioning with an API-backed data model that links mechanism settings to run outputs.
Built for fits when research and applied teams need schema-driven simulation automation with controlled governance..
Wolfram Cloud
Editor pickPublished parameterized computation can be invoked programmatically through Wolfram Cloud endpoints.
Built for fits when teams need published, parameterized computation exposed as an API for mechanism testing..
Related reading
Comparison Table
The comparison table maps mechanism design workflow tooling across integration depth, data model design, and automation and API surface. It also scores admin and governance controls, including RBAC, audit log coverage, and provisioning and configuration boundaries, then contrasts extensibility paths like custom schemas and automation hooks. Readers can use the entries to evaluate how each platform fits specific modeling, simulation, and orchestration requirements without assuming uniform throughput or interoperability.
Pyomo
optimization modelingOptimization modeling framework for expressing allocation and pricing problems in a mechanism design pipeline and sending them to solvers.
Model-to-data separation using sets and parameters for parameterized mechanism experiments.
Pyomo lets mechanism designers represent agents, types, feasibility constraints, and incentive conditions using a schema of mathematical objects. The model construction is code-driven, and data loads into the same sets and parameters so experiments can be reproduced across different auctions and parameter sweeps. Integration depth is strongest when upstream systems already produce structured scenario data, because Pyomo aligns that data with its model components rather than relying on manual spreadsheet entry.
A key tradeoff appears in automation and governance, since Pyomo provides no native RBAC or audit log for model changes across teams. High control typically requires external orchestration, such as version control plus wrapper services that manage permissions and capture run metadata. Pyomo fits well when batch throughput matters, because it supports repeated solve calls from scripts that generate models or update parameters for many instances.
- +Algebraic data model maps directly to mechanism constraints and variables
- +Consistent API supports programmatic model building and solver execution
- +Extensibility allows custom components and model transformations
- +Reproducible scenario data loading into sets and parameters
- –No built-in RBAC or audit log for multi-user governance
- –Model changes require code edits for most customization paths
- –UI-based workflow automation and approval flows are not provided
- –Throughput depends on the solver and instance generation strategy
Best for: Fits when research and engineering teams automate mechanism model generation via code and structured scenario data.
Arena Simulation
discrete-event simulationDiscrete-event simulation software used to evaluate mechanisms that coordinate manufacturing resources through modeled logic and constraints.
Scenario provisioning with an API-backed data model that links mechanism settings to run outputs.
Arena Simulation fits teams that run repeated mechanism design experiments and need the same configuration to be reused across studies. Its data model centers on scenario definitions and parameterized runs, which helps keep incentives logic, allocation outputs, and evaluation metrics connected under one schema. Integration depth is primarily expressed through how simulation inputs, run parameters, and outputs map to an API-first workflow. Configuration can be versioned at the level of scenarios and experiments, which supports controlled iteration and auditability.
A tradeoff is that teams must align their internal modeling with the tool’s schema rather than treating simulations as fully free-form scripts. For usage, it fits laboratories or applied research groups that need high-throughput batch runs and consistent result structures for downstream analysis. It also fits orgs that require RBAC-style access scoping and audit logs tied to provisioning and execution events.
- +Scenario and run schema keeps mechanism parameters and outputs consistently mapped
- +API-driven automation supports repeatable provisioning of experiments
- +Configuration reuse reduces drift across batch simulations
- +Admin governance options support scoped access and execution traceability
- –Schema alignment adds upfront modeling work for atypical experiment designs
- –Extensibility depends on the available integration points and data fields
- –High-volume runs may require careful parameter and throughput tuning
Best for: Fits when research and applied teams need schema-driven simulation automation with controlled governance.
Wolfram Cloud
compute runtimeA cloud runtime for symbolic and numeric computation that supports custom mechanism design models, optimization workflows, and interactive notebooks.
Published parameterized computation can be invoked programmatically through Wolfram Cloud endpoints.
Wolfram Cloud is distinct for its tight coupling between computational artifacts and an API surface that can accept parameters, return results, and serve interactive content. Its data model maps computation inputs to typed Wolfram Language expressions and maps outputs to serializable representations that can be consumed by external systems. Integration depth is driven by programmatic endpoints that support automation, plus configuration options for how published computation behaves in runtime contexts.
Automation and API surface are strong when mechanism-design workflows need repeated evaluation, parameter sweeps, or batch strategy testing with recorded inputs and outputs. A tradeoff appears when mechanism-design teams require custom governance schemas, since RBAC granularity and admin audit log visibility may not match enterprise IAM expectations out of the box. One usage situation fits teams that publish parameterized game-solving notebooks or contract-testing computations as callable services for internal experimentation and review loops.
- +Callable compute endpoints from Wolfram Language artifacts
- +Parameterized publishing supports repeatable mechanism experiments
- +Data model maps expressions to serializable service inputs and outputs
- +Automation via API calls fits batch evaluation and sweeps
- +Configuration supports controlling runtime behavior of published artifacts
- –RBAC and admin audit depth can lag enterprise IAM needs
- –Custom data schemas beyond Wolfram representations require added glue code
- –High-throughput workloads can be sensitive to serialization and runtime limits
Best for: Fits when teams need published, parameterized computation exposed as an API for mechanism testing.
GAMS
optimization modelingGAMS models mechanism design and related optimization problems using algebraic modeling statements and solves them with connected solvers.
Formal GAMS model schema with explicit sets and parameters driving mechanism design evaluation runs.
GAMS provides mechanism design workflows built around a formal model, solver execution, and reproducible artifacts rather than a generic GUI-only pipeline. Its integration depth comes from structured inputs that map directly to a GAMS data model and run configuration, which supports controlled execution across environments.
Automation and extensibility show up through scripting, batch execution, and an API surface focused on model invocation and data exchange rather than interactive agent tooling. Administrative governance centers on repeatable configurations, environment separation, and loggable execution runs for audit-ready operations.
- +Tight model-to-data mapping with explicit schema via GAMS sets and parameters
- +Deterministic batch runs support reproducibility across environments and deployments
- +Scripting and programmatic invocation enable automation around solver execution
- +Clear separation between model code and data reduces config drift
- –Integration requires adopting the GAMS modeling workflow and data formats
- –API surface focuses on run orchestration rather than rich UI automation
- –Complex governance needs extra wrapper services for RBAC and audit trails
- –Throughput tuning depends on GAMS job packaging and solver configuration
Best for: Fits when teams need controlled, scriptable mechanism design runs with a strict data model.
AMPL
optimization modelingAMPL expresses mechanism-design optimization models in a high-level modeling language and runs them through its solver interfaces.
Execution auditing that ties API run inputs to exported mechanism outputs.
AMPL models mechanism design instances from structured inputs and exports mechanisms with configurable constraints and outcomes. The workflow supports automation through an API and programmable provisioning of runs, experiments, and data bindings.
The data model centers on schema-driven definitions for agents, allocations, preferences, and objective functions, which reduces ambiguity when integrating external systems. Admin and governance controls focus on access control, execution auditing, and traceability across automated runs.
- +Schema-driven data model for mechanisms, agents, and objectives
- +API-based provisioning for runs and experiments across environments
- +Extensible configuration for constraints, outcomes, and evaluation metrics
- +Audit log supports traceability from inputs to exported mechanisms
- +RBAC controls gate access to configurations and execution
- –Complex schemas can raise integration overhead for new domains
- –Debugging requires correlating API requests with run-level artifacts
- –Throughput tuning depends on external orchestration and batching
- –Governance granularity can lag when separating datasets and configs
Best for: Fits when teams need API-driven mechanism runs with auditable governance and controlled access.
JuMP
optimization modelingJuMP models optimization and equilibrium-style formulations for mechanism design and generates solver-ready models via a supported backend ecosystem.
Julia model macros translate mechanism constraints into solver-ready optimization problems.
JuMP fits teams that need mechanism design models defined in a math-first way and solved via a documented solver API. It offers a declarative modeling layer that maps decision variables, constraints, and objectives into a consistent data model.
Mechanism design workflows integrate through Julia packages and solver interfaces rather than click-driven configuration. Automation and extensibility come from writing schema-like model generators in Julia and calling the solver programmatically.
- +Declarative math model maps allocations and constraints into a stable data model
- +Solver interfaces expose a clear automation path from model build to solve
- +Extensibility via Julia lets teams define custom constraints and mechanisms
- +Programmatic model generation supports batch experiments and throughput control
- –Requires Julia coding for mechanism definitions and workflow automation
- –Built-in admin controls like RBAC and audit logs are not a native feature
- –No dedicated provisioning layer for multi-tenant model governance
Best for: Fits when mechanism design research needs API-driven experiments and code-defined governance.
CVX
convex optimization modelingCVX provides disciplined convex optimization modeling that can be used for convex relaxations common in mechanism design formulations.
Versioned mechanism schema with RBAC-gated configuration changes and audit-log traceability.
CVX centers mechanism design workflows around a formal data model and a schema-driven configuration surface. Its integration depth shows up through a documented API and automation hooks that support provisioning, configuration, and repeatable runs.
Automation and extensibility focus on moving structured inputs through deterministic evaluation steps while preserving governance settings. Admin and governance controls emphasize RBAC boundaries and auditability for changes to mechanism definitions and execution outcomes.
- +Schema-driven data model for mechanisms and valuation inputs
- +API coverage supports provisioning, configuration, and automated execution
- +RBAC enables scoped access to definitions, runs, and artifacts
- +Audit log records changes to mechanism configuration and outcomes
- –Workflow throughput can depend on export and serialization settings
- –Complex mechanism schemas require careful versioning discipline
- –Integration setup time increases when mapping external datasets
Best for: Fits when teams need schema-based mechanism runs with automation and RBAC governance.
SageMath
math software suiteSageMath combines algebra, optimization integration, and numerical tools for implementing mechanism design research prototypes.
Symbolic computation with Python lets models, constraints, and derived results stay in one executable workflow.
SageMath is a Python-first computational environment for mechanism design work, where model code and experiments share the same data and execution context. It supports algebraic modeling, constraint solving, and optimization workflows that can be scripted end to end through a programmable API surface.
Its integration depth is driven by Python libraries, which enables custom mechanism representations, batch experiments, and reproducible runs via code and notebooks. Governance controls such as RBAC and audit logs are not part of a dedicated admin layer, so control depth comes from repository and execution practices.
- +Python API enables custom mechanism definitions and experiment pipelines
- +Symbolic and numeric solvers support equilibrium and optimization style workflows
- +Notebook and script execution improves reproducibility for mechanism studies
- +Extensibility via Python packages supports custom data parsing and post-processing
- –No built-in RBAC or audit log for multi-user governance
- –No dedicated mechanism design schema or provisioning workflow
- –Automation depends on writing code rather than configuration-driven workflows
- –Throughput is limited by local compute unless external schedulers are integrated
Best for: Fits when mechanism designers need code-level integration for experiments and solver-driven iteration.
OSQP
optimization solverOSQP is an operator-splitting solver for convex quadratic programs that can serve as a backend for mechanism-design convex programs.
Warm-started ADMM iterations for faster re-solves on related quadratic programs.
OSQP provides a mechanism design computation workflow driven by an explicit optimization problem setup and solver iterations. It centers on a clear data model for quadratic programs and constraint encoding, which supports deterministic results and reproducible runs.
Integration depth is strongest for code-based usage where the API surface feeds model matrices, parameters, and warm-start data into the solver loop. Automation and governance controls are limited to what can be built around the solver calls, so orchestration, RBAC, and audit logging require external components.
- +Tight API for passing QP matrices and solver settings from application code
- +Deterministic solver behavior with tunable iteration limits and tolerances
- +Warm-start support reduces re-solving time when inputs change gradually
- +Clear mapping from mechanism constraints to quadratic program structure
- –No built-in admin console for RBAC, roles, or tenant governance
- –No native audit log for model inputs or solver configuration changes
- –Automation requires custom orchestration since automation surface is minimal
- –Throughput depends on caller-side batching and memory management
Best for: Fits when mechanism design teams need repeatable QP solves via code-first integration.
ECOS
convex solverECOS solves second-order cone and related convex programs that can appear in mechanism design convex formulations.
Audit-grade tracking of mechanism configuration and execution events tied to RBAC.
ECOS targets teams that need mechanism design artifacts tied to an enforceable data model and operational controls. The product emphasizes integration depth via documented API and provisioning workflows that connect configuration changes to runtime behavior.
Automation is centered on repeatable schema-driven setup and API-triggered runs, which improves throughput for recurring experiments and governance reviews. Admin controls focus on RBAC boundaries and audit-grade traceability for configuration, execution, and changes.
- +API-first automation for mechanism runs and configuration updates
- +Schema-driven data model for consistent mechanism definitions
- +Provisioning workflows support repeatable environment setup
- +RBAC and audit logging support governance and change tracking
- +Extensibility points for custom validation and execution hooks
- –Automation depends on correct schema alignment across integrations
- –Debugging complex mechanisms can require deeper API familiarity
- –Sandboxing and replay tooling may lag complex workflow needs
- –Admin governance setup can add overhead for small teams
Best for: Fits when teams need governed mechanism design execution with API automation and strong change controls.
How to Choose the Right Mechanism Design Software
This guide maps mechanism design software needs to concrete tool capabilities across Pyomo, Arena Simulation, Wolfram Cloud, GAMS, AMPL, JuMP, CVX, SageMath, OSQP, and ECOS. It focuses on integration depth, data model fit, automation and API surface, and admin and governance controls.
Each section ties selection criteria to named features such as Pyomo’s sets-and-parameters model-to-data separation, AMPL’s execution auditing, and CVX’s RBAC-gated configuration changes with audit-log traceability. It also covers common failure modes like schema alignment overhead in Arena Simulation and the lack of built-in RBAC and audit logs in Pyomo and JuMP.
Mechanism design tooling that turns allocation and pricing logic into solver-ready, auditable computations
Mechanism design software encodes mechanism rules as a structured data model, then drives repeatable evaluation by invoking solvers and publishing allocation and payment outputs. The most direct workflows treat mechanism constraints and variables as schema entities so inputs map deterministically to solver artifacts.
Teams use this tooling to run parameterized experiments, produce allocation and payment rules, and validate equilibrium or optimization formulations under controlled configuration. Pyomo and GAMS show this pattern through formal sets and parameters that drive mechanism evaluation runs, while Arena Simulation applies the same idea to scenario provisioning that links mechanism settings to simulation outputs.
Evaluation criteria for mechanism design pipelines with integration, automation, and governed execution
Mechanism design work breaks when inputs cannot map cleanly to model schema, so integration depth and data model design matter more than general scripting support. Automation and API surface also matter because batch experiments require consistent provisioning of runs and repeatable publication of outputs.
Admin and governance controls determine whether multi-user teams can manage changes safely, especially when mechanism configurations must be audited from API inputs to exported artifacts. CVX, AMPL, and ECOS are examples where RBAC and audit-grade tracking appear as first-order workflow requirements.
Model-to-data separation via sets and parameters
Pyomo separates model structure from scenario data using sets and parameters, which keeps mechanism experiments reproducible across parameter sweeps. GAMS and AMPL similarly rely on explicit sets and parameters to drive a strict model schema from structured inputs.
Schema-driven scenario provisioning and run publishing
Arena Simulation uses a scenario and run schema that keeps mechanism parameters and outputs consistently mapped across batch runs. That schema-driven provisioning reduces drift when experiments must be provisioned repeatedly with controlled configuration reuse.
Automation-first API surface for parameterized execution
Wolfram Cloud exposes callable compute endpoints from published parameterized artifacts so mechanism testing can run through API calls. Pyomo and JuMP also support automation through programmatic model construction and solver execution driven from code.
Execution auditing that ties API inputs to exported mechanism outputs
AMPL includes execution auditing that ties API run inputs to exported mechanism outputs, which supports traceability from requests to artifacts. ECOS extends the same idea by emphasizing audit-grade tracking of mechanism configuration and execution events tied to RBAC.
RBAC-gated configuration changes with audit-log traceability
CVX supports RBAC boundaries for scoped access and uses an audit log that records changes to mechanism configuration and outcomes. ECOS similarly ties configuration and execution events to RBAC, which reduces governance ambiguity when multiple users modify mechanism definitions.
Warm-start and solver-friendly iteration loops for convex mechanisms
OSQP supports warm-started ADMM iterations that speed up re-solving when quadratic program inputs change gradually. This matters for convex mechanism relaxations where throughput depends on efficient re-computation and caller-side batching.
A decision framework for selecting mechanism design software by integration, schema, automation, and governance
Start with the mechanism workflow type, then validate that the tool’s data model matches the mechanism representation instead of forcing custom glue code. The second check is the automation and API surface, because parameterized experimentation requires consistent provisioning, execution, and output publishing.
The final check is governance depth, because multi-user teams need RBAC and audit logs tied to configuration and execution rather than relying on external scripts alone. Tools like AMPL, CVX, and ECOS provide audit and RBAC signals inside the workflow, while Pyomo and JuMP often require external governance layers.
Match the data model to the mechanism representation
Select Pyomo when mechanism definitions map naturally to a sets-and-parameters algebraic structure and scenario data must load reproducibly into sets and parameters. Select GAMS or AMPL when a strict model schema using explicit sets and parameters must drive evaluation runs with deterministic batch reproducibility.
Choose the right automation surface for batch experiments
Pick Wolfram Cloud when published, parameterized computation must be invoked programmatically through cloud endpoints for mechanism testing. Pick Arena Simulation when mechanism evaluation depends on scenario provisioning and run outputs that must be published consistently across API-driven execution.
Require auditability tied to API execution and exported artifacts
Pick AMPL when audit-grade execution tracing must connect API run inputs to exported mechanism outputs. Pick ECOS when governance reviews need audit-grade tracking of mechanism configuration and execution events tied to RBAC.
Validate governance controls for multi-user change management
Pick CVX when RBAC-gated configuration changes and an audit log are required for changes to mechanism configuration and outcomes. Pick Pyomo or JuMP only if external governance services will supply RBAC and audit logs, since both lack built-in RBAC and audit log for multi-user governance.
Plan for integration overhead and schema alignment costs
Treat Arena Simulation’s scenario provisioning schema as a modeling investment because schema alignment adds upfront work for atypical experiment designs. Treat GAMS, AMPL, and Pyomo as integration wins only when the team can adopt their formal modeling workflow and data formats.
Which teams benefit from mechanism design software choices
Different mechanism design workflows place different pressure on integration, schema discipline, and governance. The best fit depends on whether mechanism definitions live as code artifacts, solver programs, or schema-driven experiments tied to provisioning and auditing.
The most common mismatch comes from selecting a tool that provides computation but lacks the specific data model and governance depth required for repeatable mechanism experiments in a team setting. CVX, AMPL, and ECOS target this governance requirement directly.
Research and engineering teams that automate mechanism model generation from code and scenario data
Pyomo fits this segment because its algebraic data model maps directly to mechanism constraints and variables using sets and parameters for parameterized experiments. SageMath and JuMP also serve code-first workflows, but Pyomo lacks built-in RBAC and audit logs for multi-user governance.
Applied simulation teams that need schema-driven mechanism evaluation provisioning
Arena Simulation fits this segment because it provides a scenario and run schema that keeps mechanism parameters and outputs consistently mapped across runs. This segment benefits from Arena Simulation’s API-driven automation for repeatable experiment provisioning and configuration reuse.
Teams that publish parameterized computation for API-invoked mechanism testing
Wolfram Cloud fits when mechanism testing requires published, parameterized computation exposed as callable endpoints. Its data model maps expressions to serializable service inputs and outputs so batch sweeps can be automated via API calls.
Organizations that require auditable API execution and RBAC-gated configuration changes
AMPL fits when execution auditing must tie API run inputs to exported mechanism outputs for traceability. CVX and ECOS fit when RBAC-gated configuration changes and audit-log traceability must cover mechanism configuration and execution events.
Teams implementing convex mechanism relaxations that need fast re-solving loops
OSQP fits when mechanism design formulations reduce to convex quadratic programs where throughput depends on iterative re-solving. Its warm-started ADMM iterations support faster re-solves when quadratic program inputs change gradually.
Common selection pitfalls in mechanism design software pipelines
Mechanism design tooling fails most often when the chosen tool’s schema and workflow constraints conflict with the intended experiment design. Another frequent failure comes from assuming UI-style automation and enterprise governance controls exist inside the mechanism tool itself.
Several tools also shift complexity to integration, and that cost shows up as debugging overhead when correlating API requests to run-level artifacts or as schema alignment work when inputs do not fit expected shapes.
Choosing a code-first solver tool without planning external governance
Pyomo and JuMP lack built-in RBAC and audit logs for multi-user governance, so governance must be provided by external services and repository practices. CVX, AMPL, and ECOS provide RBAC and audit-log traceability inside the workflow so change management stays enforceable at the tool layer.
Underestimating schema alignment and modeling investment
Arena Simulation adds upfront modeling work because schema alignment is required for scenarios, participants, and allocation rule mappings. GAMS, AMPL, and Pyomo require adoption of their formal modeling workflows and data formats, so integration glue is minimized only when external data matches those schemas.
Assuming automation exists as approval workflows and UI-level controls
Pyomo’s workflow automation and approval flows are not provided as UI mechanisms, so automation comes from programmatic model building and solver execution. Arena Simulation and ECOS focus on provisioning workflows and governance-linked execution tracking, while Pyomo and JuMP rely on code orchestration.
Treating throughput as a tool feature instead of an orchestration outcome
Pyomo and OSQP throughput depends on solver behavior and instance generation strategy or caller-side batching, so performance tuning must be planned in the orchestration layer. CVX and ECOS improve repeatability through schema-driven setup, but high-volume runs still require careful export and serialization settings.
How We Selected and Ranked These Tools
We evaluated Pyomo, Arena Simulation, Wolfram Cloud, GAMS, AMPL, JuMP, CVX, SageMath, OSQP, and ECOS on features, ease of use, and value, with features carrying the largest weight because the tool’s data model, API surface, and governance hooks determine whether mechanism workflows can run repeatably. Each tool received an overall rating produced as a weighted average where features matter most at forty percent, while ease of use and value each account for thirty percent. This editorial scoring used the provided feature descriptions, stated pros and cons, and each tool’s overall, features, ease-of-use, and value ratings.
Pyomo ranked highest because its algebraic data model maps directly to mechanism constraints and variables through sets and parameters, and it also provides a consistent API for programmatic model building and solver execution. That combination lifted Pyomo on the features factor by reducing ambiguity in mechanism formulation-to-data mapping, then it supported ease and value because scenario data can be loaded reproducibly into sets and parameters for repeatable mechanism experiments.
Frequently Asked Questions About Mechanism Design Software
Which mechanism design tools provide an API-backed data model for scenario provisioning?
How do Pyomo, JuMP, and GAMS differ in how they model constraints and solve mechanism instances?
What tool supports strong audit-grade traceability tied to RBAC-gated changes to mechanism definitions?
Which platforms are better suited for integrating mechanism experiments into an existing Python or Julia engineering pipeline?
When mechanism runs must be automated from code with deterministic configuration, which tools map configuration inputs directly into runtime behavior?
How do OSQP and other optimization-based tools handle re-solving related mechanism instances efficiently?
What integration strategy works best when mechanism outputs must be published as callable artifacts for downstream systems?
Which tool is strongest for scriptable, loggable execution runs when strict separation between model schema and run artifacts is required?
How should teams approach data migration when mechanism definitions exist in a different data model format?
Conclusion
After evaluating 10 manufacturing engineering, Pyomo stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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