Orange “MiningTech Africa” text in a casual, handwritten style on a transparent background.
08:30
Chairs Opening Remarks
The Autonomous Mine Is No Longer a Concept — It Is an Engineering Challenge
Autonomous mining is moving from pilot deployments to large-scale operational systems. While regions such as Australia and Canada have deployed autonomous haulage and drilling fleets at scale, African operations face unique constraints including deep underground environments, legacy infrastructure, limited connectivity, workforce transition challenges, and complex safety regimes.

This opening address frames the central engineering challenge for the industry:
How do mining companies design, deploy, and scale safe, resilient, and economically viable autonomous mining systems in Africa’s unique operating environments?
09:00
Sandvik
Designing the Autonomous Mine: Systems Architecture for Integrated AI-Driven Operations
Autonomy in mining is not achieved through individual machines but through fully integrated system architectures linking fleets, sensors, networks, control systems, and AI platforms.

This session examines the system-level design of the autonomous mine.
  • Integrated autonomous fleet architectures
  • Edge computing vs cloud-based decision systems
  • Sensor fusion frameworks
  • Control system redundancy and fail-safe design
  • Interoperability between autonomous equipment platforms
Autonomous mining operations rely on a complex ecosystem of robotic equipment, AI systems, communication networks, and operational control platforms. Many mines are attempting to introduce automation technologies into environments that were never designed for autonomous operation, creating integration challenges between legacy systems and modern digital infrastructure.
This Session Explores
  • System architecture required for autonomous mining environments
  • Integration of robotics, AI platforms, and operational control systems
  • Scalability challenges in large mining operations
Technical Focus
  • Autonomous system architecture design
  • Sensor fusion frameworks for mining environments
  • Integration of autonomous equipment with fleet management systems
  • Distributed control systems for mining automation
  • Interoperability between equipment manufacturers
Learning Objectives
  • Understand how autonomous mining systems are architected at the system level
  • Identify key integration challenges in deploying autonomous technologies
  • Explore scalable architectures for future autonomous mines
09:20
Connectivity Infrastructure for Autonomous Mining Operations
Victor Thobakgale
Technology Operations Lead,
African Rainbow Minerals
Autonomous mining systems require continuous, low-latency communication between equipment fleets, sensors, and control systems. However, deep underground mining environments present significant connectivity challenges including signal attenuation, complex tunnel geometries, and limited infrastructure for high-bandwidth networks.
This Session Explores the Communication Infrastructure Required to Support
  • Underground robotic equipment
  • Autonomous haulage fleets
  • Remote operations centres
  • High-bandwidth sensor and monitoring networks
Technical Focus
  • Private LTE and emerging 5G mining networks
  • Mesh networking architectures for deep underground mines
  • Latency requirements for autonomous vehicle control
  • Edge computing for autonomous mining equipment
  • Network redundancy and fail-safe communication architectures
  • Cybersecurity frameworks for mining communication networks
Learning Objectives
  • Evaluate networking technologies suitable for underground mining environments
  • Understand latency and bandwidth requirements for autonomous mining systems
  • Explore strategies for building resilient mining communication infrastructure
09:40
From Data to Decision: Why AI Initiatives Stall in Mining Operations
Werner Swanepoel
SVP Digital,
Sibanye-Stillwater
Mining companies are investing heavily in AI, analytics, and digital transformation initiatives. Yet many programmes fail to move beyond isolated pilots or deliver sustainable operational value at scale.
The challenge is often not the technology itself — but the ability to structure, govern, contextualise, and operationalise data in ways that support real decision-making inside complex mining environments.
This Session Explores
  • Why many AI and analytics initiatives fail to scale in mining environments
  • The operational challenges of turning data into usable decision support
  • Data fragmentation across mining, plant, maintenance, and business systems
  • Aligning digital initiatives with operational realities and business priorities
  • The role of people, process, and governance in successful digital transformation
Technical & Strategic Focus
  • Data architecture and operational data integration
  • Real-time operational visibility and decision-support systems
  • Data governance, contextualisation, and quality management
  • Integrating operational technology (OT) and enterprise systems
  • AI-readiness within live mining environments
  • Practical implementation challenges in digital mining programmes
Learning Objectives
  • Understand why many AI initiatives struggle to deliver operational value
  • Explore the relationship between data quality, operational context, and decision-making
  • Identify barriers to scaling digital initiatives across mining operations
  • Learn how mining organisations can improve digital adoption and operational alignment
  • Examine practical approaches to building data-driven mining environments
10:00
Sandvik
Deploying Autonomous Haulage Fleets in Large-Scale Mining Operations
Autonomous haulage systems have delivered substantial safety and productivity benefits in large open-pit mining operations. However, scaling these systems requires overcoming significant engineering challenges related to fleet coordination, operational safety validation, and integration with existing mining processes.
This Session Explores
  • Engineering requirements for autonomous haulage deployment
  • Fleet coordination and traffic management
  • Safety validation frameworks for autonomous equipment
Technical Focus
  • Autonomous navigation systems for mining trucks
  • Collision avoidance and situational awareness technologies
  • Integration with dispatch and fleet management platforms
  • Operational control systems for autonomous fleets
  • Redundancy and fail-safe control systems
Learning Objectives
  • Understand the architecture of autonomous haulage systems
  • Explore engineering challenges involved in deploying autonomous fleets
  • Identify strategies for scaling autonomous haulage operations
10:20
Sandvik
NETWORKING BREAK
Three blank rectangular display plinths of different sizes, ideal for showcasing African Mining Automation products, set against a white background.
10:40
AI-Optimised Mine Planning
Martin Pretorius
Program Manager,
Mandela Mining Precinct
Traditional mine planning relies on static models that struggle to adapt to dynamic operational conditions. AI-driven planning systems enable continuous optimisation of mining operations.
This Session Explores
  • Machine learning applications in mine planning
  • AI-driven production scheduling
  • Optimisation of resource extraction strategies
Technical Focus
  • Reinforcement learning for mine planning
  • Predictive production modelling
  • AI-driven scheduling algorithms
  • Integration with geological modelling platforms
  • Dynamic resource allocation systems
Learning Objectives
  • Understand AI-driven mine planning technologies
  • Explore dynamic production optimisation techniques
  • Identify opportunities for AI-enabled decision support
11:00
Sandvik
Managing Hybrid Mining Fleets: Autonomous and Human-Operated Equipment
Nuhu Salifu
Vice President & Managing Director,
SANDVIK Mining & Rock Technology
Many mines will operate hybrid fleets for years, combining autonomous equipment with human-operated machinery. Ensuring safe and efficient interaction between these systems presents complex operational and engineering challenges.
This Session Explores
  • Operational frameworks for hybrid mining fleets
  • Safety protocols for mixed autonomous and human operations
  • Traffic management strategies
Technical Focus
  • Human-machine interaction systems
  • Vehicle proximity detection technologies
  • Autonomous decision-making algorithms
  • Operator awareness systems
  • Safety management frameworks
Learning Objectives
  • Understand risks associated with mixed fleet operations
  • Explore technologies enabling safe human-autonomous interaction
  • Identify strategies for managing hybrid mining fleets
11:20
Robotics for Deep-Level Underground Mining
Deep underground mining environments present extreme conditions including high temperatures, confined spaces, seismic activity, and limited connectivity. These environments require specialised robotic systems capable of operating reliably under challenging conditions.
This Session Explores
  • Robotic technologies for underground mining
  • Automation of hazardous mining tasks
  • Remote and autonomous operation of underground equipment
Technical Focus
  • Underground robotic mobility systems
  • Sensor technologies for underground navigation
  • AI-enabled robotic perception
  • Robotic drilling and material handling
  • Autonomous navigation in GPS-denied environments
Learning Objectives
  • Understand engineering challenges for underground mining robotics
  • Explore technologies enabling robotic underground mining operations
  • Identify opportunities to improve safety using robotics
11:40
Sandvik
Autonomous Drilling Systems
Drilling operations require high levels of precision and adaptability to changing geological conditions. Autonomous drilling systems must integrate sensor feedback, geological models, and machine control systems to maintain accuracy and operational efficiency.
This Session Explores
  • Automated drill rig control systems
  • Real-time geological feedback integration
  • Precision drilling technologies
Technical Focus
  • Sensor-guided drilling systems
  • AI-driven drilling optimisation
  • Rock face mapping technologies
  • Drilling automation platforms
  • Integration with mine planning systems
Learning Objectives
  • Understand the architecture of autonomous drilling systems
  • Explore technologies enabling precision drilling automation
  • Identify productivity gains from drilling automation
12:00
AI-Driven Ore Body Modelling
Jacques Nel
Geologist & Snr Consultant,
Maptek
Modern exploration generates enormous volumes of geological and geophysical data. Traditional modelling methods struggle to process these data sets efficiently, limiting the accuracy of resource estimation and mine planning.
This Session Explores
  • Machine learning techniques for geological modelling
  • AI-driven exploration analytics
  • Predictive resource estimation
Technical Focus
  • Deep learning for geological interpretation
  • Geospatial data integration platforms
  • AI-assisted resource modelling
  • Predictive exploration algorithms
  • Integration with digital mine platforms
Learning Objectives
  • Understand how AI enhances geological modelling
  • Explore machine learning applications in mineral exploration
  • Identify opportunities to improve resource estimation accuracy
12:20
Rio Tinto Logo
Predictive Maintenance for Mining Equipment
Wilhemina Ngcobo
Chief Operating Officer,
Richards Bay Minerals (Rio Tinto)
Mining equipment failures cause significant operational disruptions and safety risks. Autonomous mining systems introduce new layers of mechanical, electrical, and digital complexity that require advanced monitoring and predictive maintenance strategies.
This Session Explores
  • AI-based equipment health monitoring
  • Predictive maintenance frameworks for mining equipment
  • Integration with maintenance management systems
Technical Focus
  • Machine learning failure prediction models
  • Vibration and acoustic monitoring technologies
  • Sensor-based condition monitoring systems
  • Predictive analytics platforms for equipment maintenance
  • Integration with enterprise asset management systems
Learning Objectives
  • Understand predictive maintenance technologies for mining equipment
  • Explore machine learning approaches for failure prediction
  • Identify strategies to improve equipment reliability
12:40
Sandvik
Digital Twins for Mining Operations
Mining operations involve complex interactions between equipment fleets, geological models, and processing infrastructure. Digital twin technologies allow mining companies to simulate and optimise operations in real time.
This Session Explores
  • Digital twins of mining operations
  • Real-time operational modelling
  • Remote and autonomous operation of underground equipment
Technical Focus
  • High-fidelity simulation platforms
  • AI-enabled operational forecasting
  • Digital twin integration with mining control systems
  • Sensor-driven operational monitoring
  • Predictive production modelling
Learning Objectives
  • Understand digital twin architectures for mining operations
  • Explore simulation technologies for operational optimisation
  • Identify use cases for digital twins in mining
13:00
Sandvik
LUNCHEON NETWORKING
Minimalist exhibition stand with blank white walls, overhead spotlights, and a central counter—ideal for African Mining Automation displays.
14:00
From Geological Data to Digital Mining
Siphelele Buthelezi
Executive Manager,
Council for Geoscience
As mining operations become increasingly data-driven, the quality, accessibility, and interoperability of geological data are becoming critical enablers of modern exploration, resource modelling, mine planning, and digital mining systems.

Geological information forms the foundation upon which advanced analytics, AI-driven modelling, and integrated digital mining platforms are built. However, many mining organisations continue to face challenges related to fragmented datasets, inconsistent standards, legacy exploration records, and limited accessibility of historical geological information.
This Session Explores
  • The role of geological data in enabling digital mining systems
  • Digitisation and preservation of historical exploration data and core libraries
  • Data accessibility, interoperability, and integration challenges
  • The role of public geoscience institutions in supporting exploration and mining
  • How geological information supports modern modelling, planning, and analytics
Technical Focus
  • Geological data management systems
  • Digital geological mapping and core digitisation
  • Geospatial data integration platforms
  • Data standards and interoperability frameworks
  • Integration of geological datasets into digital mine platforms
  • AI-assisted geological interpretation and modelling
Learning Objectives
  • Understand how geological data enables modern digital mining operations
  • Explore challenges associated with geological data quality and accessibility
  • Identify best practices for managing and integrating geological datasets
  • Examine the role of public geoscience institutions in supporting the mining industry
  • Understand how digital geological infrastructure supports exploration, planning, and resource development
14:20
Sandvik
Safety Validation of Autonomous Mining Systems
Autonomous mining equipment must operate safely in highly hazardous environments. Ensuring operational safety requires rigorous validation processes, testing frameworks, and compliance with evolving safety standards.
Technical Focus
  • Functional safety frameworks for autonomous systems
  • Validation testing methodologies
  • Hazard identification and risk analysis
  • Safety-critical system design
Learning Objectives
  • Understand safety validation frameworks for autonomous mining equipment
  • Explore testing strategies for autonomous systems
  • Identify best practices for managing operational risk
14:40
Sandvik
Computer Vision Systems for Mining Safety
Mining environments present numerous safety hazards including vehicle collisions, falling debris, and dangerous working conditions. AI-driven vision systems are increasingly deployed to monitor operations and detect hazards in real time.
Technical Focus
  • Computer vision algorithms for hazard detection
  • Worker proximity detection systems
  • AI-based situational awareness platforms
Learning Objectives
  • Understand how AI vision systems improve mining safety
  • Explore applications of computer vision in hazardous environments
  • Identify deployment challenges for AI safety monitoring systems
15:00
Sandvik
Cybersecurity for Autonomous Mining Systems
Autonomous mining systems rely on highly connected networks of machines and control systems. Cybersecurity vulnerabilities could disrupt operations or compromise safety-critical systems.
Technical Focus
  • Cybersecurity frameworks for industrial control systems
  • Network intrusion detection technologies
  • Secure communication protocols
  • Risk mitigation strategies for mining operations
Learning Objectives
  • Understand cybersecurity threats facing autonomous mining operations
  • Explore strategies for securing mining control systems
  • Identify best practices for cyber-resilient mining infrastructure
15:20
Sandvik
Navigation Systems for Autonomous Mining Equipment in GPS-Denied Environments
Autonomous mining equipment operating in underground environments cannot rely on satellite-based positioning systems. Navigation systems must instead combine multiple sensing technologies to accurately determine position and orientation within complex and constantly changing underground mine layouts.

Reliable navigation is essential for ensuring safe equipment movement, collision avoidance, and accurate production operations.
This Session Explores
  • Navigation technologies for underground mining equipment
  • Positioning systems for autonomous drilling and haulage
  • Sensor fusion approaches for navigation in GPS-denied environments
Technical Focus
  • LiDAR-based mapping and localisation
  • Simultaneous localisation and mapping (SLAM) algorithms
  • Inertial navigation systems
  • Sensor fusion combining LiDAR, radar, cameras and IMUs
  • Underground positioning infrastructure
Learning Objectives
  • Understand navigation technologies used in autonomous mining equipment
  • Explore engineering approaches to GPS-denied positioning
  • Identify limitations and accuracy considerations of underground navigation systems
15:40
Sandvik
Perception Systems for Autonomous Mining Equipment
Autonomous mining equipment must perceive and interpret complex operating environments, including moving equipment, workers, changing terrain, and environmental hazards.

Achieving reliable perception in dusty, low-light, and high-vibration mining environments remains a significant engineering challenge.
This Session Explores
  • Environmental perception technologies for mining robotics
  • Hazard detection systems
  • Sensor fusion for situational awareness
Technical Focus
  • LiDAR perception systems
  • Radar sensing for harsh environments
  • Computer vision systems for mining equipment
  • AI-based object detection algorithms
  • Sensor fusion architectures for autonomous vehicles
Learning Objectives
  • Understand the sensor technologies enabling autonomous mining perception systems
  • Explore engineering challenges of perception in harsh environments
  • Identify strategies for improving situational awareness in autonomous equipment
16:00
Sandvik
Edge Computing for Autonomous Mining Equipment
Autonomous mining systems must make decisions in real time, often in environments where connectivity to cloud infrastructure is limited or unreliable.

Edge computing architectures enable AI models and control systems to operate directly on mining equipment.
This Session Explores
  • Onboard computing systems for autonomous mining equipment
  • Distributed AI architectures for mining operations
  • Real-time decision making at the edge
Technical Focus
  • GPU and AI accelerator hardware for autonomous vehicles
  • Real-time operating systems for autonomous equipment
  • Distributed AI inference systems
  • Edge-cloud data synchronisation architectures
  • Low-latency control loops for autonomous machines
Learning Objectives
  • Understand the role of edge computing in autonomous mining systems
  • Explore hardware and software architectures for onboard AI
  • Identify challenges associated with deploying AI models in mining equipment
16:20
Sandvik
AFTERNOON NETWORKING BREAK
Three plain white rectangular display plinths of varying sizes, ideal for showcasing African Mining Automation technology, with lights on the largest.
17:00
Sandvik
Interoperability Challenges Between Autonomous Mining Platforms
Most mining operations deploy equipment from multiple manufacturers. Autonomous systems from different OEMs often operate using proprietary software platforms and communication protocols, creating integration challenges for mine operators.

Achieving interoperability between equipment platforms is essential for scalable autonomous mining operations.
This Session Explores
  • Interoperability challenges between autonomous mining systems
  • Standardisation efforts in mining automation
  • Integration of multi-vendor autonomous fleets
Technical Focus
  • Industrial communication standards
  • Open mining automation platforms
  • API integration for fleet management systems
  • Cross-platform data exchange architectures
  • Autonomous fleet orchestration systems
Learning Objectives
  • Understand interoperability challenges across mining equipment platforms
  • Explore strategies for integrating multi-vendor autonomous systems
  • Identify emerging standards for mining automation
17:20
Sandvik
Designing Human–Machine Interfaces for Autonomous Mining Operations
Even highly automated mines require human supervision. Designing effective human–machine interfaces is essential for enabling operators to monitor autonomous systems, respond to anomalies, and maintain situational awareness across large mining operations.
This Session Explores
  • Control room interface design
  • Operator decision-support systems
  • Monitoring autonomous equipment fleets
Technical Focus
  • Human factors engineering for automation systems
  • Visualisation platforms for autonomous fleet monitoring
  • Alarm management systems
  • Real-time operational dashboards
  • Augmented reality for maintenance operations
Learning Objectives
  • Understand how operators interact with autonomous mining systems
  • Explore design principles for automation control interfaces
  • Identify strategies for improving operator situational awareness
17:40
Rio Tinto Logo
Engineering Remote Operations Centres for Autonomous Mines
Remote operations centres enable mining companies to control equipment fleets and monitor operations from centralised facilities located far from the mine site. Designing these centres requires sophisticated control systems, communication infrastructure, and human-machine interaction frameworks.
This Session Explores
  • Operational architectures for remote mining control centres
  • Technologies enabling remote equipment operation
  • Workforce transformation enabled by remote operations
Technical Focus
  • Supervisory control systems for mining operations
  • Real-time monitoring platforms
  • Distributed operations architectures
  • Remote equipment control technologies
  • Operational data visualisation systems
Learning Objectives
  • Understand how remote operations centres support autonomous mining
  • Explore engineering requirements for remote control systems
  • Identify operational benefits of remote mining operations
18:00
Operational Readiness for Autonomous Mining: Why Technology Alone Fails
Philip Mare
Operational Director,
Metal Management Solutions
Mining companies are investing heavily in AI, automation, and autonomous systems. Yet many deployments fail to scale beyond pilot phases — not because the technology does not work, but because operations are not fully prepared to absorb it.
The challenge is no longer simply acquiring autonomous systems. It is preparing mines, infrastructure, operating models, and workforces to operate safely and effectively in increasingly automated environments.
This Session Explores
  • Why autonomous mining projects stall after pilot deployment
  • The operational readiness required before autonomy can scale
  • How leadership teams align operations, engineering, maintenance, and technology groups
  • The role of workforce capability and organisational structure in successful deployment
  • Lessons learned from real-world implementation failures and successes
Technical & Strategic Focus:
  • Change management in operational environments
  • Readiness assessment frameworks
  • OT/IT integration maturity
  • Workforce transition and operator retraining
  • Reliability and operational continuity during deployment
  • Governance models for autonomous operations
Learning Objectives:
  • Understand the operational barriers to scaling autonomy
  • Identify readiness gaps within mining organisations
  • Explore how successful operators sequence deployment
  • Learn how operational alignment impacts ROI and adoption success
This session moves beyond technology deployment alone to examine the operational reality of autonomy in mining — where success depends not simply on implementing advanced systems, but on aligning infrastructure, workforce capability, operational processes, and organisational readiness to deliver safe, reliable, and scalable mining operations in practice.
18:20
From Strategy to Execution: How Mining Leaders Navigate Autonomy Under Real-World Constraints
Sandile Siyaya
Executive Vice President,
Sasol South Africa
Despite rapid advances in automation, AI, and digital mining technologies, fully autonomous mining operations remain the exception rather than the norm.
For large-scale operators, the challenge is not a lack of technology — it is the complex reality of implementation within live, high-risk, production-critical environments.
The transition to autonomy is not a technology deployment exercise — it is a strategic, operational, and financial transformation programme.
This Session Explores:
  • What “autonomy” realistically means for large-scale mining organisations today
  • How mining executives prioritise automation within broader operational and capital strategies
  • The sequencing of deployment across underground, surface, and processing systems
  • How legacy operations and infrastructure impact autonomy roadmaps
  • The operational risks of implementation — and how they are mitigated
  • The role of integrated planning in aligning autonomy with production and business outcomes
Learning Objectives:
  • Understand how senior mining executives approach autonomy at an enterprise level
  • Identify the real-world constraints that shape automation strategies
  • Explore how capital is allocated across competing operational priorities
  • Learn how to balance production, safety, and transformation simultaneously
  • Gain insight into how integrated planning drives successful implementation
  • Understand what separates pilot projects from scalable, sustainable deployment
This session moves beyond vision statements and technology narratives to examine the operational reality of autonomy in mining — where success is defined not by innovation alone, but by the ability to deliver safe, reliable, and economically viable production at scale.
18:40
From Mine Modernisation to Operational Autonomy: How Mining Leaders Prioritise Safety, Performance, Capital Discipline, and Real-World Implementation
Shane Ryan
COO, Trinity Metals Group
For leaders responsible for running and scaling multi-asset operations, the challenge is not simply adopting new technologies — it is delivering them within live production environments while simultaneously developing new assets, maintaining safety, and meeting commercial targets.

Across critical minerals operations and emerging mining regions, this challenge is amplified by:
  • evolving infrastructure and supply chains
  • the need to develop processing capability alongside extraction
  • increasing global demand for responsibly sourced materials
  • workforce development and capability building
  • and the pressure to deliver both short-term production and long-term growth
The transition from modernisation to autonomy is therefore not a linear journey — it is a dynamic balancing act between operational performance, project development, and strategic transformation.
This Session Explores:
  • How mining companies prioritise modernisation and automation alongside active production and project development
  • The sequencing of transformation across underground operations, processing plants, and new project pipelines
  • How safety, performance, and SHEC commitments are maintained during periods of operational change
  • The role of partnerships (EPC/EPCM, technology providers, government stakeholders) in enabling execution
  • How mining leaders navigate infrastructure constraints and regional complexities in emerging markets
  • Aligning operational delivery with global demand for critical minerals and evolving supply chains
19:00
Sandvik
Chair's Closing Remarks
19:15
Sandvik
Networking Drinks Reception & Fork Buffet
18:20
Sandvik
From Strategy to Execution: How Mining Leaders Navigate Autonomy Under Real-World Constraints
Sandile Siyaya
Executive Vice President, Sasol South Africa
Despite rapid advances in automation, AI, and digital mining technologies, fully autonomous mining operations remain the exception rather than the norm.
For large-scale operators, the challenge is not a lack of technology — it is the complex reality of implementation within live, high-risk, production-critical environments.
The transition to autonomy is not a technology deployment exercise — it is a strategic, operational, and financial transformation programme.
This Session Explores:
  • What “autonomy” realistically means for large-scale mining organisations today
  • How mining executives prioritise automation within broader operational and capital strategies
  • The sequencing of deployment across underground, surface, and processing systems
  • How legacy operations and infrastructure impact autonomy roadmaps
  • The operational risks of implementation — and how they are mitigated
  • The role of integrated planning in aligning autonomy with production and business outcomes
Learning Objectives:
  • Understand how senior mining executives approach autonomy at an enterprise level
  • Identify the real-world constraints that shape automation strategies
  • Explore how capital is allocated across competing operational priorities
  • Learn how to balance production, safety, and transformation simultaneously
  • Gain insight into how integrated planning drives successful implementation
  • Understand what separates pilot projects from scalable, sustainable deployment
This session moves beyond vision statements and technology narratives to examine the operational reality of autonomy in mining — where success is defined not by innovation alone, but by the ability to deliver safe, reliable, and economically viable production at scale.
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