use ruv_fann::prelude::*;
use semantic_cartan_matrix::*;

// Medical imaging pipeline with distributed analysis
struct MedicalImagingSystem {
    cartan_processor: SemanticCartanSystem,
    diagnostic_swarm: SwarmCoordinator,
    gpu_accelerator: CudaWasmTranspiler,
}

impl MedicalImagingSystem {
    async fn analyze_scan(&mut self, scan_data: &[u8]) -> DiagnosticReport {
        // 1. Distributed preprocessing with swarm
        let preprocessed = self.diagnostic_swarm
            .coordinate_preprocessing(scan_data)
            .await?;
        
        // 2. Feature extraction with Cartan attention
        let features = self.cartan_processor
            .extract_medical_features(&preprocessed)?;
        
        // 3. GPU-accelerated classification
        let diagnosis = self.gpu_accelerator
            .classify_pathology(&features)
            .await?;
        
        DiagnosticReport {
            confidence: diagnosis.confidence,
            findings: diagnosis.pathologies,
            processing_time_ms: self.get_processing_time(),
        }
    }
}

// Autonomous vehicle perception system
struct AutonomousPerceptionSystem {
    sensor_fusion_swarm: SensorFusionSwarm,
    object_detection: CartanVisionNetwork,
    path_planning: SwarmPathPlanner,
}

impl AutonomousPerceptionSystem {
    async fn process_sensor_data(&mut self, sensors: SensorBundle) -> DrivingDecision {
        // Parallel sensor processing with specialized agents
        let lidar_analysis = self.sensor_fusion_swarm
            .spawn_lidar_agent(sensors.lidar_data);
        let camera_analysis = self.sensor_fusion_swarm
            .spawn_vision_agent(sensors.camera_data);
        let radar_analysis = self.sensor_fusion_swarm
            .spawn_radar_agent(sensors.radar_data);
        
        // Coordinate results with swarm intelligence
        let fused_perception = self.sensor_fusion_swarm
            .coordinate_fusion([lidar_analysis, camera_analysis, radar_analysis])
            .await?;
        
        // Real-time path planning
        self.path_planning.compute_safe_trajectory(fused_perception).await
    }
}

// Industrial inspection system with swarm agents
struct IndustrialInspectionSystem {
    inspection_swarm: QualityControlSwarm,
    defect_classifier: CartanClassifier,
    real_time_metrics: MetricsCollector,
}

impl IndustrialInspectionSystem {
    async fn inspect_product(&mut self, product_images: Vec<Image>) -> QualityReport {
        // Spawn specialized inspection agents
        let surface_inspector = self.inspection_swarm
            .spawn_surface_defect_agent();
        let dimensional_inspector = self.inspection_swarm
            .spawn_dimensional_accuracy_agent();
        let color_inspector = self.inspection_swarm
            .spawn_color_consistency_agent();
        
        // Parallel inspection with coordination
        let results = self.inspection_swarm.coordinate_parallel_inspection([
            surface_inspector.inspect(&product_images),
            dimensional_inspector.measure(&product_images),
            color_inspector.analyze(&product_images),
        ]).await?;
        
        // Generate comprehensive quality report
        QualityReport::from_inspection_results(results)
    }
}

// Large-scale document analysis with swarm coordination
struct DocumentAnalysisSystem {
    document_swarm: DocumentProcessingSwarm,
    semantic_extractor: SemanticCartanProcessor,
    multi_lang_support: MultiLanguageTransformer,
}

impl DocumentAnalysisSystem {
    async fn analyze_document_batch(&mut self, documents: Vec<Document>) -> AnalysisReport {
        // Spawn specialized analysis agents
        let content_agents = self.document_swarm.spawn_content_extractors(8);
        let sentiment_agents = self.document_swarm.spawn_sentiment_analyzers(4);
        let entity_agents = self.document_swarm.spawn_entity_extractors(6);
        
        // Distributed processing with load balancing
        let results = self.document_swarm.coordinate_batch_processing(
            documents,
            [content_agents, sentiment_agents, entity_agents]
        ).await?;
        
        // Semantic aggregation with Cartan matrix
        let semantic_summary = self.semantic_extractor
            .aggregate_semantic_understanding(&results)?;
        
        AnalysisReport {
            processed_documents: documents.len(),
            key_insights: semantic_summary.insights,
            sentiment_distribution: results.sentiment_stats,
            processing_time: self.get_processing_metrics(),
        }
    }
}

// Conversational AI with swarm-based reasoning
struct ConversationalAISystem {
    conversation_swarm: ConversationSwarm,
    context_manager: SwarmMemoryManager,
    personality_engine: PersonalitySwarm,
}

impl ConversationalAISystem {
    async fn process_conversation(&mut self, user_input: &str) -> ConversationResponse {
        // Spawn reasoning agents for different aspects
        let intent_agent = self.conversation_swarm.spawn_intent_analyzer();
        let context_agent = self.conversation_swarm.spawn_context_maintainer();
        let response_agent = self.conversation_swarm.spawn_response_generator();
        let personality_agent = self.personality_engine.spawn_personality_adapter();
        
        // Parallel reasoning with swarm coordination
        let reasoning_results = self.conversation_swarm.coordinate_reasoning([
            intent_agent.analyze_intent(user_input),
            context_agent.maintain_context(user_input),
            personality_agent.adapt_personality_tone(),
        ]).await?;
        
        // Generate contextually aware response
        let response = response_agent.generate_response(
            user_input,
            reasoning_results,
            self.context_manager.get_conversation_history()
        ).await?;
        
        ConversationResponse {
            message: response.text,
            confidence: response.confidence,
            context_maintained: true,
            personality_consistency: response.personality_score,
        }
    }
}

// AI-powered code generation and analysis
struct CodeIntelligenceSystem {
    code_analysis_swarm: CodeAnalysisSwarm,
    generation_engine: CodeGenerationEngine,
    compilation_validator: RealTimeValidator,
}

impl CodeIntelligenceSystem {
    async fn analyze_and_generate(&mut self, requirements: CodeRequirements) -> CodeSolution {
        // Specialized code analysis agents
        let architecture_agent = self.code_analysis_swarm.spawn_architecture_analyzer();
        let pattern_agent = self.code_analysis_swarm.spawn_pattern_detector();
        let security_agent = self.code_analysis_swarm.spawn_security_auditor();
        let performance_agent = self.code_analysis_swarm.spawn_performance_optimizer();
        
        // Parallel code analysis
        let analysis = self.code_analysis_swarm.coordinate_analysis([
            architecture_agent.analyze_requirements(&requirements),
            pattern_agent.detect_applicable_patterns(&requirements),
            security_agent.identify_security_requirements(&requirements),
            performance_agent.analyze_performance_constraints(&requirements),
        ]).await?;
        
        // Generate optimized code solution
        let generated_code = self.generation_engine
            .generate_code_solution(&requirements, &analysis).await?;
        
        // Real-time validation and compilation
        let validation_results = self.compilation_validator
            .validate_and_compile(&generated_code).await?;
        
        CodeSolution {
            source_code: generated_code,
            architecture_score: analysis.architecture_quality,
            security_score: analysis.security_rating,
            performance_metrics: validation_results.performance,
            compilation_status: validation_results.compilation_success,
        }
    }
}

// Warehouse automation with swarm-coordinated robots
struct WarehouseRobotSwarm {
    robot_coordinator: HierarchicalSwarmCoordinator,
    path_planner: DistributedPathPlanner,
    task_scheduler: IntelligentTaskScheduler,
    collision_avoidance: SwarmCollisionAvoidance,
}

impl WarehouseRobotSwarm {
    async fn coordinate_warehouse_operations(&mut self, orders: Vec<Order>) -> OperationPlan {
        // Spawn robot agents for different zones
        let picking_robots = self.robot_coordinator
            .spawn_robot_agents(RobotType::Picker, 12);
        let transport_robots = self.robot_coordinator
            .spawn_robot_agents(RobotType::Transporter, 8);
        let sorting_robots = self.robot_coordinator
            .spawn_robot_agents(RobotType::Sorter, 6);
        
        // Distributed task allocation with load balancing
        let task_allocation = self.task_scheduler.allocate_tasks_optimally(
            &orders,
            [&picking_robots, &transport_robots, &sorting_robots]
        ).await?;
        
        // Coordinate swarm movement with collision avoidance
        let movement_plan = self.path_planner.plan_coordinated_movement(
            &task_allocation,
            &self.collision_avoidance.get_dynamic_constraints()
        ).await?;
        
        // Execute coordinated warehouse operations
        self.robot_coordinator.execute_coordinated_plan(&movement_plan).await
    }
}

// Autonomous drone fleet with weather-adaptive swarm intelligence
struct AutonomousDroneFleet {
    flight_coordinator: AerialSwarmCoordinator,
    weather_adaptor: WeatherAdaptiveSystem,
    battery_optimizer: EnergyOptimizationSystem,
    airspace_manager: AirspaceCoordinationSystem,
}

impl AutonomousDroneFleet {
    async fn execute_delivery_mission(&mut self, deliveries: Vec<DeliveryTask>) -> MissionResults {
        // Weather-aware route planning
        let weather_data = self.weather_adaptor.get_real_time_conditions().await?;
        let adapted_routes = self.weather_adaptor
            .adapt_routes_for_conditions(&deliveries, &weather_data)?;
        
        // Energy-optimized swarm coordination
        let energy_plan = self.battery_optimizer
            .optimize_energy_consumption(&adapted_routes).await?;
        
        // Coordinate airspace with other aircraft
        let airspace_clearance = self.airspace_manager
            .coordinate_flight_paths(&energy_plan).await?;
        
        // Execute coordinated delivery mission
        let mission_execution = self.flight_coordinator
            .execute_swarm_mission(&airspace_clearance).await?;
        
        MissionResults {
            deliveries_completed: mission_execution.successful_deliveries,
            energy_efficiency: energy_plan.efficiency_score,
            weather_adaptations: weather_data.adaptations_made,
            flight_time_total: mission_execution.total_flight_time,
        }
    }
}

// Industrial process control with predictive swarm intelligence
struct IndustrialControlSystem {
    process_controller: DistributedControlSwarm,
    predictive_maintenance: PredictiveMaintenanceSystem,
    safety_monitor: SafetyMonitoringSwarm,
    optimization_engine: ProcessOptimizationEngine,
}

impl IndustrialControlSystem {
    async fn control_industrial_process(&mut self, process_state: ProcessState) -> ControlResponse {
        // Distributed sensor monitoring with specialized agents
        let temperature_agents = self.process_controller.spawn_temperature_controllers(8);
        let pressure_agents = self.process_controller.spawn_pressure_controllers(6);
        let flow_agents = self.process_controller.spawn_flow_controllers(4);
        let quality_agents = self.process_controller.spawn_quality_monitors(3);
        
        // Predictive maintenance analysis
        let maintenance_prediction = self.predictive_maintenance
            .analyze_equipment_health(&process_state).await?;
        
        // Safety monitoring with swarm coordination
        let safety_status = self.safety_monitor
            .coordinate_safety_checks(&process_state).await?;
        
        // Process optimization with multi-objective goals
        let optimization_plan = self.optimization_engine.optimize_process(
            &process_state,
            &maintenance_prediction,
            &safety_status
        ).await?;
        
        // Execute coordinated control actions
        self.process_controller.execute_control_plan(&optimization_plan).await
    }
}

// Decentralized autonomous trading with quantum-resistant security
struct AutonomousTradingSystem {
    trading_agents: DecentralizedTradingSwarm,
    risk_manager: QuantumResistantRiskManager,
    market_analyzer: MultiMarketAnalysisEngine,
    ruv_treasury: RuvTokenTreasury,
}

impl AutonomousTradingSystem {
    async fn execute_trading_strategy(&mut self, market_data: MarketData) -> TradingResults {
        // Spawn specialized trading agents
        let arbitrage_agents = self.trading_agents.spawn_arbitrage_hunters(6);
        let trend_agents = self.trading_agents.spawn_trend_followers(8);
        let momentum_agents = self.trading_agents.spawn_momentum_traders(4);
        let risk_agents = self.trading_agents.spawn_risk_assessors(3);
        
        // Multi-market analysis with swarm intelligence
        let market_analysis = self.market_analyzer.coordinate_analysis([
            arbitrage_agents.analyze_price_discrepancies(&market_data),
            trend_agents.identify_market_trends(&market_data),
            momentum_agents.detect_momentum_shifts(&market_data),
        ]).await?;
        
        // Quantum-resistant risk assessment
        let risk_assessment = self.risk_manager
            .assess_portfolio_risk(&market_analysis).await?;
        
        // Execute trades with rUv token economics
        let trading_plan = self.ruv_treasury
            .optimize_capital_allocation(&risk_assessment).await?;
        
        self.trading_agents.execute_coordinated_trades(&trading_plan).await
    }
}

// Real-time fraud detection with swarm-based behavioral analysis
struct FraudDetectionSystem {
    behavior_analyzers: BehavioralAnalysisSwarm,
    transaction_monitors: TransactionMonitoringSwarm,
    pattern_detectors: PatternDetectionEngine,
    alert_coordinator: AlertCoordinationSystem,
}

impl FraudDetectionSystem {
    async fn analyze_transaction(&mut self, transaction: Transaction) -> FraudAssessment {
        // Spawn behavioral analysis agents
        let spending_pattern_agent = self.behavior_analyzers.spawn_spending_analyzer();
        let location_pattern_agent = self.behavior_analyzers.spawn_location_analyzer();
        let time_pattern_agent = self.behavior_analyzers.spawn_temporal_analyzer();
        let merchant_pattern_agent = self.behavior_analyzers.spawn_merchant_analyzer();
        
        // Real-time transaction monitoring
        let monitoring_results = self.transaction_monitors.coordinate_monitoring([
            spending_pattern_agent.analyze_spending_behavior(&transaction),
            location_pattern_agent.analyze_location_patterns(&transaction),
            time_pattern_agent.analyze_temporal_patterns(&transaction),
            merchant_pattern_agent.analyze_merchant_interactions(&transaction),
        ]).await?;
        
        // Pattern detection with ensemble methods
        let fraud_indicators = self.pattern_detectors
            .detect_fraud_patterns(&monitoring_results).await?;
        
        // Coordinate alert response
        let assessment = self.alert_coordinator
            .assess_fraud_risk(&fraud_indicators).await?;
        
        FraudAssessment {
            risk_score: assessment.risk_level,
            fraud_probability: assessment.fraud_probability,
            recommended_action: assessment.action,
            detection_latency_ms: assessment.processing_time,
        }
    }
}

// Algorithmic investment management with ESG and risk optimization
struct InvestmentManagementSystem {
    portfolio_optimizers: PortfolioOptimizationSwarm,
    esg_analyzers: ESGAnalysisSwarm,
    risk_diversifiers: RiskDiversificationEngine,
    performance_trackers: PerformanceTrackingSystem,
}

impl InvestmentManagementSystem {
    async fn optimize_portfolio(&mut self, portfolio: Portfolio) -> OptimizationResults {
        // Spawn portfolio optimization agents
        let return_optimizer = self.portfolio_optimizers.spawn_return_maximizer();
        let risk_minimizer = self.portfolio_optimizers.spawn_risk_minimizer();
        let esg_optimizer = self.esg_analyzers.spawn_esg_scorer();
        let diversification_agent = self.risk_diversifiers.spawn_diversification_optimizer();
        
        // Multi-objective portfolio optimization
        let optimization_results = self.portfolio_optimizers.coordinate_optimization([
            return_optimizer.maximize_expected_returns(&portfolio),
            risk_minimizer.minimize_portfolio_risk(&portfolio),
            esg_optimizer.optimize_esg_scores(&portfolio),
            diversification_agent.optimize_diversification(&portfolio),
        ]).await?;
        
        // Performance tracking and attribution
        let performance_metrics = self.performance_trackers
            .analyze_portfolio_performance(&optimization_results).await?;
        
        OptimizationResults {
            optimized_allocation: optimization_results.allocation,
            expected_return: optimization_results.return_forecast,
            risk_metrics: optimization_results.risk_analysis,
            esg_score: optimization_results.esg_rating,
            performance_attribution: performance_metrics,
        }
    }
}

// Molecular dynamics simulation with SIMD optimization and distributed computing
struct MolecularDynamicsSystem {
    simd_processor: SIMDOptimizedProcessor,
    distributed_simulator: DistributedSimulationSwarm,
    protein_folder: ProteinFoldingEngine,
    force_calculator: ForceCalculationEngine,
}

impl MolecularDynamicsSystem {
    async fn simulate_molecular_system(&mut self, molecule: MolecularSystem) -> SimulationResults {
        // SIMD-optimized force calculations
        let force_calculations = self.simd_processor
            .calculate_intermolecular_forces(&molecule)
            .await?;
        
        // Distributed simulation with specialized agents
        let simulation_agents = self.distributed_simulator.spawn_simulation_agents([
            ("bond_dynamics", 8),
            ("electrostatic_interactions", 6), 
            ("van_der_waals_forces", 4),
            ("solvent_effects", 3),
        ]);
        
        // Coordinate distributed MD simulation
        let simulation_steps = self.distributed_simulator
            .coordinate_md_simulation(&molecule, &force_calculations, 1_000_000)
            .await?;
        
        // Protein folding analysis
        let folding_analysis = self.protein_folder
            .analyze_folding_pathway(&simulation_steps).await?;
        
        SimulationResults {
            trajectory: simulation_steps,
            folding_pathway: folding_analysis.pathway,
            energy_landscape: folding_analysis.energy_surface,
            simulation_time_ns: 1000.0, // 1 microsecond simulated
            computation_time_hours: simulation_steps.computation_time,
        }
    }
}

// Climate modeling with multi-scale atmospheric simulation
struct ClimateModelingSystem {
    atmospheric_simulator: AtmosphericSimulationSwarm,
    ocean_modeler: OceanCurrentModeler,
    ensemble_forecaster: EnsembleForecastingEngine,
    data_assimilator: DataAssimilationSystem,
}

impl ClimateModelingSystem {
    async fn run_climate_simulation(&mut self, initial_conditions: ClimateState) -> ClimateProjection {
        // Multi-scale atmospheric simulation
        let atmospheric_agents = self.atmospheric_simulator.spawn_agents([
            ("global_circulation", 12),
            ("regional_weather", 8),
            ("local_phenomena", 16),
            ("cloud_dynamics", 6),
        ]);
        
        // Ocean-atmosphere coupling
        let ocean_interactions = self.ocean_modeler
            .simulate_ocean_atmosphere_coupling(&initial_conditions)
            .await?;
        
        // Ensemble forecasting with uncertainty quantification
        let ensemble_runs = self.ensemble_forecaster
            .run_ensemble_simulations(&initial_conditions, 50)
            .await?;
        
        // Data assimilation from satellite observations
        let assimilated_data = self.data_assimilator
            .assimilate_observational_data(&ensemble_runs)
            .await?;
        
        ClimateProjection {
            temperature_projections: assimilated_data.temperature_trends,
            precipitation_patterns: assimilated_data.precipitation_forecasts,
            extreme_events: assimilated_data.extreme_weather_probability,
            uncertainty_bounds: ensemble_runs.uncertainty_quantification,
            simulation_duration_years: 100,
        }
    }
}

// Quantum chemistry calculations with hybrid classical-quantum computing
struct QuantumChemistrySystem {
    quantum_simulator: QuantumCircuitSimulator,
    classical_optimizer: ClassicalOptimizationSwarm,
    reaction_predictor: ChemicalReactionPredictor,
    energy_calculator: ElectronicStructureCalculator,
}

impl QuantumChemistrySystem {
    async fn calculate_molecular_properties(&mut self, molecule: Molecule) -> QuantumResults {
        // Quantum circuit simulation for electronic structure
        let quantum_state = self.quantum_simulator
            .simulate_molecular_hamiltonian(&molecule)
            .await?;
        
        // Classical optimization of quantum parameters
        let optimization_agents = self.classical_optimizer.spawn_agents([
            ("variational_parameters", 6),
            ("basis_set_optimization", 4),
            ("correlation_energy", 3),
        ]);
        
        let optimized_parameters = self.classical_optimizer
            .optimize_quantum_parameters(&quantum_state)
            .await?;
        
        // Energy calculations with optimized parameters
        let ground_state_energy = self.energy_calculator
            .calculate_ground_state_energy(&molecule, &optimized_parameters)
            .await?;
        
        // Reaction pathway prediction
        let reaction_pathways = self.reaction_predictor
            .predict_reaction_mechanisms(&molecule)
            .await?;
        
        QuantumResults {
            ground_state_energy,
            excited_states: quantum_state.excited_state_energies,
            reaction_barriers: reaction_pathways.activation_energies,
            molecular_orbitals: quantum_state.orbital_coefficients,
            chemical_accuracy: true, // Within 1 kcal/mol
        }
    }
}

// Mobile health monitoring with edge AI processing
#[wasm_bindgen]
pub struct MobileHealthMonitor {
    vital_signs_processor: VitalSignsProcessor,
    anomaly_detector: AnomalyDetectionEngine,
    privacy_preserver: PrivacyPreservingAI,
    emergency_coordinator: EmergencyResponseSystem,
}

#[wasm_bindgen]
impl MobileHealthMonitor {
    #[wasm_bindgen(constructor)]
    pub fn new() -> Self {
        Self {
            vital_signs_processor: VitalSignsProcessor::new(),
            anomaly_detector: AnomalyDetectionEngine::new(),
            privacy_preserver: PrivacyPreservingAI::new(),
            emergency_coordinator: EmergencyResponseSystem::new(),
        }
    }
    
    #[wasm_bindgen]
    pub async fn process_sensor_data(&mut self, sensor_data: &[f32]) -> HealthAssessment {
        // Real-time vital signs processing on device
        let vital_signs = self.vital_signs_processor
            .extract_vital_signs(sensor_data)
            .await?;
        
        // Privacy-preserving anomaly detection
        let anomaly_score = self.anomaly_detector
            .detect_health_anomalies(&vital_signs)
            .await?;
        
        // Emergency response coordination if needed
        if anomaly_score.is_critical() {
            self.emergency_coordinator
                .trigger_emergency_response(&vital_signs)
                .await?;
        }
        
        HealthAssessment {
            heart_rate: vital_signs.heart_rate,
            blood_pressure: vital_signs.blood_pressure,
            oxygen_saturation: vital_signs.spo2,
            anomaly_score: anomaly_score.score,
            emergency_status: anomaly_score.is_critical(),
            privacy_preserved: true,
        }
    }
}

// Smart city IoT system with autonomous edge computing
struct SmartCityEdgeSystem {
    traffic_optimizers: TrafficOptimizationSwarm,
    energy_managers: EnergyManagementSwarm,
    environmental_monitors: EnvironmentalMonitoringSwarm,
    emergency_responders: EmergencyResponseSwarm,
}

impl SmartCityEdgeSystem {
    async fn coordinate_city_operations(&mut self, city_data: CityOperationalData) -> CityResponse {
        // Traffic optimization with real-time coordination
        let traffic_optimization = self.traffic_optimizers
            .optimize_traffic_flow(&city_data.traffic_sensors)
            .await?;
        
        // Energy management across city infrastructure
        let energy_optimization = self.energy_managers
            .coordinate_energy_distribution(&city_data.energy_grid)
            .await?;
        
        // Environmental monitoring and response
        let environmental_response = self.environmental_monitors
            .monitor_air_quality_and_noise(&city_data.environmental_sensors)
            .await?;
        
        // Emergency response coordination
        let emergency_status = self.emergency_responders
            .coordinate_emergency_services(&city_data.emergency_data)
            .await?;
        
        CityResponse {
            traffic_adjustments: traffic_optimization.signal_timings,
            energy_distribution: energy_optimization.grid_adjustments,
            environmental_alerts: environmental_response.alerts,
            emergency_responses: emergency_status.active_responses,
            overall_city_efficiency: self.calculate_efficiency_metrics(),
        }
    }
}

// Industrial IoT edge analytics with predictive maintenance
struct IndustrialIoTEdgeSystem {
    sensor_processors: SensorProcessingSwarm,
    maintenance_predictors: PredictiveMaintenanceEngine,
    quality_controllers: QualityControlSwarm,
    production_optimizers: ProductionOptimizationEngine,
}

impl IndustrialIoTEdgeSystem {
    async fn process_industrial_data(&mut self, sensor_data: IndustrialSensorData) -> IndustrialResponse {
        // Real-time sensor data processing
        let sensor_analysis = self.sensor_processors
            .process_multi_sensor_data(&sensor_data)
            .await?;
        
        // Predictive maintenance analysis
        let maintenance_predictions = self.maintenance_predictors
            .predict_equipment_failures(&sensor_analysis)
            .await?;
        
        // Quality control monitoring
        let quality_assessment = self.quality_controllers
            .assess_production_quality(&sensor_analysis)
            .await?;
        
        // Production optimization recommendations
        let optimization_plan = self.production_optimizers
            .optimize_production_parameters(&sensor_analysis)
            .await?;
        
        IndustrialResponse {
            maintenance_schedule: maintenance_predictions.recommended_actions,
            quality_adjustments: quality_assessment.recommended_adjustments,
            production_optimizations: optimization_plan.parameter_changes,
            efficiency_metrics: self.calculate_production_efficiency(),
            alert_status: maintenance_predictions.urgent_alerts,
        }
    }
}