pub struct SignalTransductionSystem {
    // Molecular-level quantum signal processing
    pub quantum_signal_processor: QuantumSignalProcessor,
    
    // Cellular-level biological signal integration
    pub biological_signal_integrator: BiologicalSignalIntegrator,
    
    // Network-level signal propagation
    pub network_signal_propagator: NetworkSignalPropagator,
    
    // System-level consciousness emergence
    pub consciousness_emergence_orchestrator: ConsciousnessEmergenceOrchestrator,
    
    // Cross-modal integration hub
    pub cross_modal_integrator: CrossModalIntegrator,
    
    // Specialized processing coordinator
    pub specialized_processing_coordinator: SpecializedProcessingCoordinator,
}

impl SignalTransductionSystem {
    pub fn transduce_signal(&mut self, input_signal: InputSignal) 
        -> TransductionResult {
        
        // Process through quantum signal processor
        let quantum_processed = self.quantum_signal_processor
            .process_quantum_signal(input_signal);
        
        // Integrate through biological signal mechanisms
        let biologically_integrated = self.biological_signal_integrator
            .integrate_biological_signals(quantum_processed);
        
        // Propagate through neural network
        let network_propagated = self.network_signal_propagator
            .propagate_network_signals(biologically_integrated);
        
        // Coordinate specialized processing
        let specialized_processed = self.specialized_processing_coordinator
            .coordinate_specialized_processing(network_propagated);
        
        // Integrate across modalities
        let cross_modal_integrated = self.cross_modal_integrator
            .integrate_cross_modal_signals(specialized_processed);
        
        // Orchestrate consciousness emergence
        let consciousness_emerged = self.consciousness_emergence_orchestrator
            .orchestrate_consciousness_emergence(cross_modal_integrated);
        
        TransductionResult::new(consciousness_emerged)
    }
}

pub struct QuantumSignalProcessor {
    // Collective ion field signal encoding
    pub ion_field_encoder: CollectiveIonFieldEncoder,
    
    // Hardware oscillation coupling
    pub hardware_oscillation_coupler: HardwareOscillationCoupler,
    
    // Environment-assisted quantum transport
    pub enaqt_signal_processor: ENAQTSignalProcessor,
    
    // Fire-wavelength signal optimization
    pub fire_wavelength_signal_optimizer: FireWavelengthSignalOptimizer,
    
    // Quantum coherence signal maintainer  
    pub quantum_coherence_maintainer: QuantumCoherenceSignalMaintainer,
}

impl QuantumSignalProcessor {
    pub fn process_quantum_signal(&mut self, input: InputSignal) 
        -> QuantumProcessedSignal {
        
        // Encode signal in collective ion field
        let ion_encoded = self.ion_field_encoder.encode_signal_in_ion_field(input);
        
        // Couple to hardware oscillations for stability
        let hardware_coupled = self.hardware_oscillation_coupler
            .couple_signal_to_hardware(ion_encoded);
        
        // Process through environment-assisted quantum transport
        let enaqt_processed = self.enaqt_signal_processor
            .process_signal_enaqt(hardware_coupled);
        
        // Optimize for fire-wavelength resonance (600-700nm)
        let fire_optimized = self.fire_wavelength_signal_optimizer
            .optimize_signal_fire_resonance(enaqt_processed);
        
        // Maintain quantum coherence throughout processing
        let coherence_maintained = self.quantum_coherence_maintainer
            .maintain_signal_coherence(fire_optimized);
        
        QuantumProcessedSignal::new(coherence_maintained)
    }
}

pub struct BiologicalSignalIntegrator {
    // Quantum-to-biological signal conversion
    pub quantum_bio_converter: QuantumToBiologicalConverter,
    
    // Hodgkin-Huxley dynamics processor
    pub hodgkin_huxley_processor: QuantumEnhancedHodgkinHuxleyProcessor,
    
    // Action potential generator
    pub action_potential_generator: ActionPotentialGenerator,
    
    // Synaptic signal processor
    pub synaptic_signal_processor: SynapticSignalProcessor,
    
    // Metabolic constraint enforcer
    pub metabolic_constraint_enforcer: MetabolicConstraintEnforcer,
}

impl BiologicalSignalIntegrator {
    pub fn integrate_biological_signals(&mut self, quantum_signal: QuantumProcessedSignal) 
        -> BiologicalIntegratedSignal {
        
        // Convert quantum signal to biological membrane dynamics
        let membrane_dynamics = self.quantum_bio_converter
            .convert_quantum_to_membrane_dynamics(quantum_signal);
        
        // Process through quantum-enhanced Hodgkin-Huxley dynamics
        let hodgkin_huxley_processed = self.hodgkin_huxley_processor
            .process_hh_dynamics(membrane_dynamics);
        
        // Generate action potentials if threshold exceeded
        let action_potentials = self.action_potential_generator
            .generate_action_potentials(hodgkin_huxley_processed);
        
        // Process synaptic signaling
        let synaptic_processed = self.synaptic_signal_processor
            .process_synaptic_signals(action_potentials);
        
        // Enforce metabolic constraints (ATP availability)
        let metabolically_constrained = self.metabolic_constraint_enforcer
            .enforce_metabolic_constraints(synaptic_processed);
        
        BiologicalIntegratedSignal::new(metabolically_constrained)
    }
}

pub struct NetworkSignalPropagator {
    // Local network propagation
    pub local_network_propagator: LocalNetworkPropagator,
    
    // Regional network integration
    pub regional_network_integrator: RegionalNetworkIntegrator,
    
    // Global network orchestration
    pub global_network_orchestrator: GlobalNetworkOrchestrator,
    
    // Network topology manager
    pub topology_manager: NetworkTopologyManager,
    
    // Signal routing system
    pub signal_router: NetworkSignalRouter,
}

impl NetworkSignalPropagator {
    pub fn propagate_network_signals(&mut self, biological_signal: BiologicalIntegratedSignal) 
        -> NetworkPropagatedSignal {
        
        // Determine optimal routing strategy
        let routing_strategy = self.signal_router
            .determine_routing_strategy(biological_signal.signal_type);
        
        // Propagate through local networks first
        let local_propagated = self.local_network_propagator
            .propagate_local_signals(biological_signal, routing_strategy);
        
        // Integrate across regional networks
        let regional_integrated = self.regional_network_integrator
            .integrate_regional_signals(local_propagated);
        
        // Orchestrate global network responses
        let global_orchestrated = self.global_network_orchestrator
            .orchestrate_global_signals(regional_integrated);
        
        // Update network topology based on activity patterns
        self.topology_manager.update_topology(global_orchestrated.activity_patterns);
        
        NetworkPropagatedSignal::new(global_orchestrated)
    }
}

pub struct LocalNetworkPropagator {
    // Local circuit dynamics
    pub local_circuits: Vec<LocalCircuit>,
    
    // Lateral inhibition mechanisms
    pub lateral_inhibition: LateralInhibitionMechanism,
    
    // Local oscillatory synchronization
    pub local_oscillatory_sync: LocalOscillatorySynchronization,
    
    // Local plasticity mechanisms
    pub local_plasticity: LocalPlasticityMechanism,
}

impl LocalNetworkPropagator {
    pub fn propagate_local_signals(&mut self, signal: BiologicalIntegratedSignal, 
        routing: RoutingStrategy) -> LocalPropagatedSignal {
        
        // Distribute signal across local circuits
        let circuit_responses: Vec<CircuitResponse> = self.local_circuits
            .iter_mut()
            .map(|circuit| circuit.process_signal(signal.clone()))
            .collect();
        
        // Apply lateral inhibition for competition
        let inhibition_modulated = self.lateral_inhibition
            .apply_lateral_inhibition(circuit_responses);
        
        // Synchronize local oscillations
        let oscillatory_synchronized = self.local_oscillatory_sync
            .synchronize_local_oscillations(inhibition_modulated);
        
        // Update local synaptic weights
        self.local_plasticity.update_local_weights(oscillatory_synchronized.activity_pattern);
        
        LocalPropagatedSignal::new(oscillatory_synchronized)
    }
}

pub struct RegionalNetworkIntegrator {
    // Regional integration modules
    pub integration_modules: Vec<RegionalIntegrationModule>,
    
    // Cross-regional connectivity
    pub cross_regional_connections: CrossRegionalConnectivity,
    
    // Regional oscillatory coordination
    pub regional_oscillatory_coordinator: RegionalOscillatoryCoordinator,
    
    // Regional attention mechanisms
    pub regional_attention: RegionalAttentionMechanism,
}

impl RegionalNetworkIntegrator {
    pub fn integrate_regional_signals(&mut self, local_signals: Vec<LocalPropagatedSignal>) 
        -> RegionalIntegratedSignal {
        
        // Integrate signals within each regional module
        let module_integrations: Vec<ModuleIntegration> = self.integration_modules
            .iter_mut()
            .zip(local_signals.chunks(local_signals.len() / self.integration_modules.len()))
            .map(|(module, local_chunk)| {
                module.integrate_local_signals(local_chunk.to_vec())
            })
            .collect();
        
        // Coordinate cross-regional connectivity
        let cross_regional_coordinated = self.cross_regional_connections
            .coordinate_cross_regional_signals(module_integrations);
        
        // Coordinate regional oscillations
        let regionally_synchronized = self.regional_oscillatory_coordinator
            .coordinate_regional_oscillations(cross_regional_coordinated);
        
        // Apply regional attention mechanisms
        let attention_modulated = self.regional_attention
            .apply_regional_attention(regionally_synchronized);
        
        RegionalIntegratedSignal::new(attention_modulated)
    }
}

pub struct SpecializedProcessingCoordinator {
    // Autobahn probabilistic reasoning coordinator
    pub autobahn_coordinator: AutobahnCoordinator,
    
    // Heihachi fire-emotion coordinator
    pub heihachi_coordinator: HeihachiCoordinator,
    
    // Helicopter visual understanding coordinator
    pub helicopter_coordinator: HelicopterCoordinator,
    
    // Izinyoka metacognitive coordinator
    pub izinyoka_coordinator: IzinyokaCoordinator,
    
    // Kwasa-Kwasa semantic coordinator
    pub kwasa_kwasa_coordinator: KwasaKwasaCoordinator,
    
    // Four Sided Triangle validation coordinator
    pub four_sided_triangle_coordinator: FourSidedTriangleCoordinator,
    
    // Processing arbitration system
    pub processing_arbitrator: ProcessingArbitrator,
}

impl SpecializedProcessingCoordinator {
    pub fn coordinate_specialized_processing(&mut self, 
        network_signal: NetworkPropagatedSignal) -> SpecializedProcessedSignal {
        
        // Determine which specialized processors should handle the signal
        let processing_assignments = self.processing_arbitrator
            .determine_processing_assignments(network_signal.signal_characteristics);
        
        // Coordinate parallel processing across specialized systems
        let mut specialized_results = Vec::new();
        
        // Process through Autobahn for probabilistic reasoning
        if processing_assignments.requires_probabilistic_reasoning {
            let autobahn_result = self.autobahn_coordinator
                .coordinate_probabilistic_processing(network_signal.clone());
            specialized_results.push(SpecializedResult::Autobahn(autobahn_result));
        }
        
        // Process through Heihachi for fire-emotion analysis
        if processing_assignments.requires_fire_emotion_processing {
            let heihachi_result = self.heihachi_coordinator
                .coordinate_fire_emotion_processing(network_signal.clone());
            specialized_results.push(SpecializedResult::Heihachi(heihachi_result));
        }
        
        // Process through Helicopter for visual understanding
        if processing_assignments.requires_visual_processing {
            let helicopter_result = self.helicopter_coordinator
                .coordinate_visual_processing(network_signal.clone());
            specialized_results.push(SpecializedResult::Helicopter(helicopter_result));
        }
        
        // Process through Kwasa-Kwasa for semantic understanding
        if processing_assignments.requires_semantic_processing {
            let kwasa_kwasa_result = self.kwasa_kwasa_coordinator
                .coordinate_semantic_processing(network_signal.clone());
            
            // Validate through Four Sided Triangle
            let validated_result = self.four_sided_triangle_coordinator
                .validate_thought_structure(kwasa_kwasa_result);
            specialized_results.push(SpecializedResult::KwasaKwasa(validated_result));
        }
        
        // Orchestrate through Izinyoka for metacognitive control
        let metacognitive_orchestrated = self.izinyoka_coordinator
            .orchestrate_metacognitive_processing(specialized_results);
        
        SpecializedProcessedSignal::new(metacognitive_orchestrated)
    }
}

pub struct HeihachiCoordinator {
    // Fire pattern signal extractors
    pub fire_pattern_extractors: Vec<FirePatternExtractor>,
    
    // Emotional signal mappers
    pub emotional_signal_mappers: Vec<EmotionalSignalMapper>,
    
    // Fire-wavelength signal resonators
    pub fire_wavelength_resonators: Vec<FireWavelengthResonator>,
    
    // Emotional oscillation encoders
    pub emotional_oscillation_encoders: Vec<EmotionalOscillationEncoder>,
}

impl HeihachiCoordinator {
    pub fn coordinate_fire_emotion_processing(&mut self, signal: NetworkPropagatedSignal) 
        -> HeihachiProcessedSignal {
        
        // Extract fire patterns from network signal
        let fire_patterns: Vec<FirePattern> = self.fire_pattern_extractors
            .iter_mut()
            .map(|extractor| extractor.extract_fire_patterns(signal.clone()))
            .collect();
        
        // Map fire patterns to emotional content
        let emotional_mappings: Vec<EmotionalMapping> = fire_patterns
            .iter()
            .zip(self.emotional_signal_mappers.iter_mut())
            .map(|(pattern, mapper)| mapper.map_fire_to_emotion(pattern.clone()))
            .collect();
        
        // Optimize for fire-wavelength resonance (600-700nm)
        let resonance_optimized: Vec<ResonanceOptimizedSignal> = emotional_mappings
            .iter()
            .zip(self.fire_wavelength_resonators.iter_mut())
            .map(|(mapping, resonator)| resonator.optimize_fire_resonance(mapping.clone()))
            .collect();
        
        // Encode in emotional oscillations
        let oscillation_encoded: Vec<EmotionalOscillationSignal> = resonance_optimized
            .iter()
            .zip(self.emotional_oscillation_encoders.iter_mut())
            .map(|(optimized, encoder)| encoder.encode_emotional_oscillations(optimized.clone()))
            .collect();
        
        // Integrate all emotional processing results
        let integrated_emotional_signal = self.integrate_emotional_signals(oscillation_encoded);
        
        HeihachiProcessedSignal::new(integrated_emotional_signal)
    }
}

pub struct HelicopterCoordinator {
    // Visual reconstruction processors
    pub reconstruction_processors: Vec<VisualReconstructionProcessor>,
    
    // Understanding validation systems  
    pub understanding_validators: Vec<UnderstandingValidator>,
    
    // Visual feature encoders
    pub visual_feature_encoders: Vec<VisualFeatureEncoder>,
    
    // Reconstruction fidelity assessors
    pub fidelity_assessors: Vec<ReconstructionFidelityAssessor>,
}

impl HelicopterCoordinator {
    pub fn coordinate_visual_processing(&mut self, signal: NetworkPropagatedSignal) 
        -> HelicopterProcessedSignal {
        
        // Process visual data through autonomous reconstruction
        let reconstruction_results: Vec<ReconstructionResult> = self.reconstruction_processors
            .iter_mut()
            .map(|processor| processor.autonomous_reconstruction(signal.visual_data.clone()))
            .collect();
        
        // Validate understanding through reconstruction fidelity
        let fidelity_assessments: Vec<FidelityAssessment> = reconstruction_results
            .iter()
            .zip(self.fidelity_assessors.iter_mut())
            .map(|(result, assessor)| assessor.assess_reconstruction_fidelity(result.clone()))
            .collect();
        
        // Filter results based on fidelity thresholds
        let validated_reconstructions: Vec<ValidatedReconstruction> = fidelity_assessments
            .iter()
            .zip(self.understanding_validators.iter_mut())
            .filter_map(|(assessment, validator)| {
                validator.validate_understanding(assessment.clone())
            })
            .collect();
        
        // Encode visual features for neural processing
        let visual_encodings: Vec<VisualNeuralEncoding> = validated_reconstructions
            .iter()
            .zip(self.visual_feature_encoders.iter_mut())
            .map(|(validated, encoder)| encoder.encode_visual_features(validated.clone()))
            .collect();
        
        // Integrate visual understanding signals
        let integrated_visual_signal = self.integrate_visual_signals(visual_encodings);
        
        HelicopterProcessedSignal::new(integrated_visual_signal)
    }
}

pub struct CrossModalIntegrator {
    // Cross-modal binding mechanisms
    pub binding_mechanisms: Vec<CrossModalBindingMechanism>,
    
    // Temporal synchronization systems
    pub temporal_synchronizers: Vec<TemporalSynchronizer>,
    
    // Feature correlation analyzers
    pub feature_correlators: Vec<FeatureCorrelator>,
    
    // Multi-modal coherence calculators
    pub coherence_calculators: Vec<MultiModalCoherenceCalculator>,
    
    // Integrated representation generators
    pub representation_generators: Vec<IntegratedRepresentationGenerator>,
}

impl CrossModalIntegrator {
    pub fn integrate_cross_modal_signals(&mut self, 
        specialized_signals: SpecializedProcessedSignal) -> CrossModalIntegratedSignal {
        
        // Extract signals from different modalities
        let visual_signals = specialized_signals.extract_visual_signals();
        let auditory_signals = specialized_signals.extract_auditory_signals();  
        let fire_emotion_signals = specialized_signals.extract_fire_emotion_signals();
        let semantic_signals = specialized_signals.extract_semantic_signals();
        let probabilistic_signals = specialized_signals.extract_probabilistic_signals();
        
        // Bind cross-modal features
        let cross_modal_bindings: Vec<CrossModalBinding> = self.binding_mechanisms
            .iter_mut()
            .map(|mechanism| {
                mechanism.bind_cross_modal_features(
                    visual_signals.clone(),
                    auditory_signals.clone(),
                    fire_emotion_signals.clone(),
                    semantic_signals.clone(),
                    probabilistic_signals.clone()
                )
            })
            .collect();
        
        // Synchronize temporal aspects across modalities
        let temporally_synchronized: Vec<TemporallySynchronizedBinding> = cross_modal_bindings
            .iter()
            .zip(self.temporal_synchronizers.iter_mut())
            .map(|(binding, synchronizer)| {
                synchronizer.synchronize_temporal_aspects(binding.clone())
            })
            .collect();
        
        // Calculate cross-modal coherence
        let coherence_measurements: Vec<CoherenceMeasurement> = temporally_synchronized
            .iter()
            .zip(self.coherence_calculators.iter_mut())
            .map(|(synchronized, calculator)| {
                calculator.calculate_multi_modal_coherence(synchronized.clone())
            })
            .collect();
        
        // Generate integrated representations
        let integrated_representations: Vec<IntegratedRepresentation> = coherence_measurements
            .iter()
            .zip(self.representation_generators.iter_mut())
            .map(|(coherence, generator)| {
                generator.generate_integrated_representation(coherence.clone())
            })
            .collect();
        
        // Combine all integrated representations
        let unified_representation = self.unify_representations(integrated_representations);
        
        CrossModalIntegratedSignal::new(unified_representation)
    }
}

pub struct TemporalBindingMechanism {
    // Oscillatory binding systems
    pub oscillatory_binders: Vec<OscillatoryBinder>,
    
    // Temporal window analyzers
    pub temporal_window_analyzers: Vec<TemporalWindowAnalyzer>,
    
    // Sequence detectors
    pub sequence_detectors: Vec<SequenceDetector>,
    
    // Temporal coherence maintainers
    pub temporal_coherence_maintainers: Vec<TemporalCoherenceMaintainer>,
}

impl TemporalBindingMechanism {
    pub fn bind_temporal_sequences(&mut self, 
        cross_modal_signal: CrossModalIntegratedSignal) -> TemporallyBoundSignal {
        
        // Analyze temporal windows for binding opportunities
        let temporal_windows: Vec<TemporalWindow> = self.temporal_window_analyzers
            .iter_mut()
            .map(|analyzer| analyzer.analyze_temporal_windows(cross_modal_signal.clone()))
            .collect();
        
        // Detect temporal sequences within windows
        let detected_sequences: Vec<DetectedSequence> = temporal_windows
            .iter()
            .zip(self.sequence_detectors.iter_mut())
            .map(|(window, detector)| detector.detect_sequences(window.clone()))
            .collect();
        
        // Bind sequences using oscillatory mechanisms
        let oscillatory_bound: Vec<OscillatoryBoundSequence> = detected_sequences
            .iter()
            .zip(self.oscillatory_binders.iter_mut())
            .map(|(sequence, binder)| binder.bind_with_oscillations(sequence.clone()))
            .collect();
        
        // Maintain temporal coherence across bound sequences
        let coherence_maintained: Vec<CoherentTemporalSequence> = oscillatory_bound
            .iter()
            .zip(self.temporal_coherence_maintainers.iter_mut())
            .map(|(bound, maintainer)| maintainer.maintain_coherence(bound.clone()))
            .collect();
        
        // Integrate temporally bound sequences
        let integrated_temporal_binding = self.integrate_temporal_sequences(coherence_maintained);
        
        TemporallyBoundSignal::new(integrated_temporal_binding)
    }
}

pub struct ConsciousnessEmergenceOrchestrator {
    // Global workspace system
    pub global_workspace: GlobalWorkspace,
    
    // Integrated information calculators
    pub integrated_information_calculators: Vec<IntegratedInformationCalculator>,
    
    // Quantum coherence orchestrators
    pub quantum_coherence_orchestrators: Vec<QuantumCoherenceOrchestrator>,
    
    // Consciousness metrics evaluators
    pub consciousness_metrics_evaluators: Vec<ConsciousnessMetricsEvaluator>,
    
    // Conscious experience synthesizers
    pub conscious_experience_synthesizers: Vec<ConsciousExperienceSynthesizer>,
}

impl ConsciousnessEmergenceOrchestrator {
    pub fn orchestrate_consciousness_emergence(&mut self, 
        temporally_bound_signal: TemporallyBoundSignal) -> ConsciousExperienceSignal {
        
        // Make information globally accessible
        let globally_accessible = self.global_workspace
            .make_globally_accessible(temporally_bound_signal);
        
        // Calculate integrated information (Φ-like measures)
        let integrated_information: Vec<IntegratedInformation> = self.integrated_information_calculators
            .iter_mut()
            .map(|calculator| calculator.calculate_integrated_information(globally_accessible.clone()))
            .collect();
        
        // Orchestrate quantum coherence for consciousness substrate
        let quantum_orchestrated: Vec<QuantumOrchestratedState> = integrated_information
            .iter()
            .zip(self.quantum_coherence_orchestrators.iter_mut())
            .map(|(info, orchestrator)| {
                orchestrator.orchestrate_quantum_coherence(info.clone())
            })
            .collect();
        
        // Evaluate consciousness metrics
        let consciousness_metrics: Vec<ConsciousnessMetrics> = quantum_orchestrated
            .iter()
            .zip(self.consciousness_metrics_evaluators.iter_mut())
            .map(|(orchestrated, evaluator)| {
                evaluator.evaluate_consciousness_metrics(orchestrated.clone())
            })
            .collect();
        
        // Synthesize conscious experience
        let conscious_experiences: Vec<ConsciousExperience> = consciousness_metrics
            .iter()
            .zip(self.conscious_experience_synthesizers.iter_mut())
            .map(|(metrics, synthesizer)| {
                synthesizer.synthesize_conscious_experience(metrics.clone())
            })
            .collect();
        
        // Integrate conscious experiences into unified signal
        let unified_conscious_experience = self.unify_conscious_experiences(conscious_experiences);
        
        ConsciousExperienceSignal::new(unified_conscious_experience)
    }
}

pub struct QuantumCoherenceOrchestrator {
    // Collective ion field coordinators
    pub ion_field_coordinators: Vec<CollectiveIonFieldCoordinator>,
    
    // Hardware oscillation synchronizers
    pub hardware_synchronizers: Vec<HardwareOscillationSynchronizer>,
    
    // Fire-wavelength coherence enhancers
    pub fire_coherence_enhancers: Vec<FireCoherenceEnhancer>,
    
    // ENAQT optimization engines
    pub enaqt_optimizers: Vec<ENAQTOptimizer>,
    
    // Coherent consciousness state generators
    pub coherent_state_generators: Vec<CoherentConsciousnessStateGenerator>,
}

impl QuantumCoherenceOrchestrator {
    pub fn orchestrate_quantum_coherence(&mut self, 
        integrated_info: IntegratedInformation) -> QuantumOrchestratedState {
        
        // Coordinate collective ion fields across neural units
        let coordinated_fields: Vec<CoordinatedIonField> = self.ion_field_coordinators
            .iter_mut()
            .map(|coordinator| coordinator.coordinate_fields(integrated_info.clone()))
            .collect();
        
        // Synchronize with hardware oscillations
        let hardware_synchronized: Vec<HardwareSynchronizedField> = coordinated_fields
            .iter()
            .zip(self.hardware_synchronizers.iter_mut())
            .map(|(field, synchronizer)| {
                synchronizer.synchronize_with_hardware(field.clone())
            })
            .collect();
        
        // Enhance coherence with fire-wavelength coupling
        let fire_enhanced: Vec<FireEnhancedCoherence> = hardware_synchronized
            .iter()
            .zip(self.fire_coherence_enhancers.iter_mut())
            .map(|(synchronized, enhancer)| {
                enhancer.enhance_with_fire_coupling(synchronized.clone())
            })
            .collect();
        
        // Optimize with environment-assisted quantum transport
        let enaqt_optimized: Vec<ENAQTOptimizedCoherence> = fire_enhanced
            .iter()
            .zip(self.enaqt_optimizers.iter_mut())
            .map(|(enhanced, optimizer)| {
                optimizer.optimize_environmental_coupling(enhanced.clone())
            })
            .collect();
        
        // Generate coherent consciousness state
        let coherent_states: Vec<CoherentConsciousnessState> = enaqt_optimized
            .iter()
            .zip(self.coherent_state_generators.iter_mut())
            .map(|(optimized, generator)| {
                generator.generate_coherent_state(optimized.clone())
            })
            .collect();
        
        // Unify coherent states into orchestrated consciousness
        let unified_coherent_state = self.unify_coherent_states(coherent_states);
        
        QuantumOrchestratedState::new(unified_coherent_state)
    }
}

pub struct AdaptiveSignalRouter {
    // Signal pathway optimizers
    pub pathway_optimizers: Vec<SignalPathwayOptimizer>,
    
    // Bottleneck detectors
    pub bottleneck_detectors: Vec<SignalBottleneckDetector>,
    
    // Load balancers
    pub load_balancers: Vec<SignalLoadBalancer>,
    
    // Performance monitors
    pub performance_monitors: Vec<SignalPerformanceMonitor>,
}

impl AdaptiveSignalRouter {
    pub fn optimize_signal_flow(&mut self, 
        system_state: SystemState) -> OptimizedRoutingConfiguration {
        
        // Detect signal processing bottlenecks
        let bottlenecks: Vec<SignalBottleneck> = self.bottleneck_detectors
            .iter_mut()
            .map(|detector| detector.detect_bottlenecks(system_state.clone()))
            .collect();
        
        // Optimize signal pathways to avoid bottlenecks
        let optimized_pathways: Vec<OptimizedPathway> = bottlenecks
            .iter()
            .zip(self.pathway_optimizers.iter_mut())
            .map(|(bottleneck, optimizer)| {
                optimizer.optimize_pathway_around_bottleneck(bottleneck.clone())
            })
            .collect();
        
        // Balance signal loads across processing units
        let load_balanced: Vec<LoadBalancedConfiguration> = optimized_pathways
            .iter()
            .zip(self.load_balancers.iter_mut())
            .map(|(pathway, balancer)| {
                balancer.balance_signal_loads(pathway.clone())
            })
            .collect();
        
        // Monitor performance of optimized configurations
        let performance_metrics: Vec<PerformanceMetrics> = load_balanced
            .iter()
            .zip(self.performance_monitors.iter_mut())
            .map(|(config, monitor)| {
                monitor.monitor_performance(config.clone())
            })
            .collect();
        
        // Generate final optimized routing configuration
        let final_configuration = self.generate_final_configuration(
            load_balanced,
            performance_metrics
        );
        
        OptimizedRoutingConfiguration::new(final_configuration)
    }
}