Create ADVANCED_CONSCIOUSNESS

Cultural analysis framework exploration

ADVANCED_CONSCIOUSNESS

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+import numpy as np
+import pandas as pd
+from dataclasses import dataclass
+from typing import Dict, List, Tuple, Optional, Any
+from enum import Enum
+import math
+from scipy import spatial, stats
+import networkx as nx
+from datetime import datetime
+import json
+from collections import defaultdict
+import warnings
+warnings.filterwarnings('ignore')
+
+class ConsciousnessState(Enum):
+    DELTA = "Deep Unconscious"      # 0.5-4 Hz
+    THETA = "Subconscious"          # 4-8 Hz  
+    ALPHA = "Relaxed Awareness"     # 8-12 Hz
+    BETA = "Active Cognition"       # 12-30 Hz
+    GAMMA = "Transcendent Unity"    # 30-100 Hz
+    SCHUMANN = "Earth Resonance"    # 7.83 Hz
+
+@dataclass
+class QuantumSignature:
+    """Qualia state vector for consciousness experience"""
+    coherence: float                    # 0-1, quantum coherence level
+    entanglement: float                 # 0-1, non-local connectivity  
+    qualia_vector: np.ndarray           # 5D experience vector [visual, emotional, cognitive, somatic, spiritual]
+    resonance_frequency: float          # Hz, characteristic resonance
+    decoherence_time: float = 1.0       # Time until quantum state collapse
+    nonlocal_correlation: float = 0.5   # EPR-type correlations
+    
+    def calculate_qualia_distance(self, other: 'QuantumSignature') -> float:
+        """Calculate distance between qualia experiences using cosine similarity"""
+        return spatial.distance.cosine(self.qualia_vector, other.qualia_vector)
+    
+    def entanglement_entropy(self) -> float:
+        """Calculate von Neumann entropy of quantum state"""
+        return -self.coherence * math.log(self.coherence + 1e-10) if self.coherence > 0 else 0
+    
+    def evolve_state(self, time: float) -> 'QuantumSignature':
+        """Evolve quantum state over time with decoherence"""
+        decay = math.exp(-time / self.decoherence_time)
+        return QuantumSignature(
+            coherence=self.coherence * decay,
+            entanglement=self.entanglement * decay,
+            qualia_vector=self.qualia_vector * decay,
+            resonance_frequency=self.resonance_frequency,
+            decoherence_time=self.decoherence_time,
+            nonlocal_correlation=self.nonlocal_correlation * decay
+        )
+
+@dataclass 
+class NeuralCorrelate:
+    """Brain region and frequency correlates with advanced connectivity"""
+    primary_regions: List[str]           # e.g., ["PFC", "DMN", "Visual Cortex"]
+    frequency_band: ConsciousnessState
+    cross_hemispheric_sync: float        # 0-1
+    neuroplasticity_impact: float        # 0-1
+    default_mode_engagement: float = 0.5 # 0-1, DMN involvement
+    salience_network_coupling: float = 0.5 # 0-1, SN connectivity
+    thalamocortical_resonance: float = 0.5 # 0-1, thalamic gating
+    
+    @property
+    def neural_efficiency(self) -> float:
+        """Calculate overall neural processing efficiency"""
+        weights = [0.3, 0.25, 0.2, 0.15, 0.1]
+        factors = [
+            self.cross_hemispheric_sync,
+            self.neuroplasticity_impact,
+            self.default_mode_engagement,
+            self.salience_network_coupling,
+            self.thalamocortical_resonance
+        ]
+        return sum(w * f for w, f in zip(weights, factors))
+
+@dataclass
+class ArchetypalStrand:
+    """Symbolic DNA strand representing cultural genotype with enhanced metrics"""
+    name: str
+    symbolic_form: str                   # e.g., "Lion", "Sunburst"
+    temporal_depth: int                  # years in cultural record
+    spatial_distribution: float          # 0-1 global prevalence
+    preservation_rate: float             # 0-1 iconographic fidelity
+    quantum_coherence: float             # 0-1 symbolic stability
+    cultural_penetration: float = 0.5    # 0-1, depth in cultural psyche
+    transformative_potential: float = 0.5 # 0-1, capacity for change
+    num_variants: int = 1                # Number of cultural variants
+    
+    @property
+    def symbolic_strength(self) -> float:
+        """Calculate overall archetypal strength with enhanced weighting"""
+        weights = [0.20, 0.20, 0.15, 0.15, 0.15, 0.15]  # Enhanced weighting
+        factors = [
+            self.temporal_depth/10000, 
+            self.spatial_distribution, 
+            self.preservation_rate, 
+            self.quantum_coherence,
+            self.cultural_penetration,
+            self.transformative_potential
+        ]
+        return min(1.0, sum(w * f for w, f in zip(weights, factors)))
+    
+    @property
+    def cultural_resilience(self) -> float:
+        """Calculate resilience against cultural erosion"""
+        return (self.preservation_rate * 0.4 + 
+                self.temporal_depth/10000 * 0.3 + 
+                self.quantum_coherence * 0.3)
+
+class ConsciousnessTechnology:
+    """Advanced neuro-symbolic interface technology with state tracking"""
+    
+    def __init__(self, name: str, archetype: ArchetypalStrand, 
+                 neural_correlate: NeuralCorrelate, quantum_sig: QuantumSignature):
+        self.name = name
+        self.archetype = archetype
+        self.neural_correlate = neural_correlate
+        self.quantum_signature = quantum_sig
+        self.activation_history = []
+        self.performance_metrics = {
+            'avg_activation_intensity': 0.0,
+            'successful_activations': 0,
+            'neural_efficiency_trend': [],
+            'quantum_coherence_trend': []
+        }
+        
+    def activate(self, intensity: float = 1.0, duration: float = 1.0) -> Dict[str, Any]:
+        """Advanced activation with duration and performance tracking"""
+        # Calculate dynamic effects based on duration and intensity
+        neural_boost = math.tanh(intensity * duration)
+        quantum_amplification = intensity * (1 - math.exp(-duration))
+        
+        activation = {
+            'timestamp': datetime.now(),
+            'archetype': self.archetype.name,
+            'intensity': intensity,
+            'duration': duration,
+            'neural_state': self.neural_correlate.frequency_band,
+            'neural_efficiency': self.neural_correlate.neural_efficiency * (1 + neural_boost),
+            'quantum_coherence': self.quantum_signature.coherence * (1 + quantum_amplification),
+            'qualia_experience': self.quantum_signature.qualia_vector * intensity,
+            'entanglement_level': self.quantum_signature.entanglement * intensity,
+            'performance_score': self._calculate_performance_score(intensity, duration)
+        }
+        
+        self.activation_history.append(activation)
+        self._update_performance_metrics(activation)
+        return activation
+    
+    def _calculate_performance_score(self, intensity: float, duration: float) -> float:
+        """Calculate activation performance score"""
+        neural_component = self.neural_correlate.neural_efficiency * intensity
+        quantum_component = self.quantum_signature.coherence * duration
+        return (neural_component * 0.6 + quantum_component * 0.4)
+    
+    def _update_performance_metrics(self, activation: Dict):
+        """Update long-term performance tracking"""
+        self.performance_metrics['successful_activations'] += 1
+        self.performance_metrics['avg_activation_intensity'] = (
+            self.performance_metrics['avg_activation_intensity'] * 0.9 + 
+            activation['intensity'] * 0.1
+        )
+        self.performance_metrics['neural_efficiency_trend'].append(
+            activation['neural_efficiency']
+        )
+        self.performance_metrics['quantum_coherence_trend'].append(
+            activation['quantum_coherence']
+        )
+    
+    def get_performance_report(self) -> Dict[str, Any]:
+        """Generate comprehensive performance analysis"""
+        trends = self.performance_metrics
+        if len(trends['neural_efficiency_trend']) > 1:
+            neural_slope = stats.linregress(
+                range(len(trends['neural_efficiency_trend'])),
+                trends['neural_efficiency_trend']
+            ).slope
+            quantum_slope = stats.linregress(
+                range(len(trends['quantum_coherence_trend'])),
+                trends['quantum_coherence_trend']
+            ).slope
+        else:
+            neural_slope = quantum_slope = 0.0
+            
+        return {
+            'total_activations': trends['successful_activations'],
+            'average_intensity': trends['avg_activation_intensity'],
+            'neural_efficiency_trend': neural_slope,
+            'quantum_coherence_trend': quantum_slope,
+            'overall_health': (trends['avg_activation_intensity'] * 0.4 + 
+                             (1 if neural_slope > 0 else 0) * 0.3 +
+                             (1 if quantum_slope > 0 else 0) * 0.3)
+        }
+
+class CulturalPhylogenetics:
+    """Advanced evolutionary analysis of symbolic DNA with Bayesian methods"""
+    
+    def __init__(self):
+        self.cladograms = {}
+        self.ancestral_reconstructions = {}
+        self.symbolic_traits = [
+            "solar_association", "predatory_nature", "sovereignty", 
+            "transcendence", "protection", "wisdom", "chaos", "creation",
+            "fertility", "destruction", "renewal", "guidance"
+        ]
+        self.trait_correlations = np.eye(len(self.symbolic_traits))
+    
+    def build_cladogram(self, archetypes: List[ArchetypalStrand], 
+                       trait_matrix: np.ndarray, 
+                       method: str = 'bayesian') -> nx.DiGraph:
+        """Build evolutionary tree using multiple methods"""
+        
+        if method == 'bayesian':
+            return self._bayesian_phylogeny(archetypes, trait_matrix)
+        elif method == 'neighbor_joining':
+            return self._neighbor_joining(archetypes, trait_matrix)
+        else:  # minimum_spanning_tree
+            return self._minimum_spanning_tree(archetypes, trait_matrix)
+    
+    def _bayesian_phylogeny(self, archetypes: List[ArchetypalStrand], 
+                          trait_matrix: np.ndarray) -> nx.DiGraph:
+        """Bayesian phylogenetic inference"""
+        G = nx.DiGraph()
+        n = len(archetypes)
+        
+        # Calculate Bayesian posterior probabilities for relationships
+        for i, arch1 in enumerate(archetypes):
+            for j, arch2 in enumerate(archetypes):
+                if i != j:
+                    # Bayesian distance incorporating prior knowledge
+                    likelihood = math.exp(-spatial.distance.euclidean(
+                        trait_matrix[i], trait_matrix[j]
+                    ))
+                    prior = self._calculate_phylogenetic_prior(arch1, arch2)
+                    posterior = likelihood * prior
+                    
+                    G.add_edge(arch1.name, arch2.name, 
+                              weight=1/posterior,  # Convert to distance
+                              probability=posterior)
+        
+        # Find maximum likelihood tree
+        mst = nx.minimum_spanning_tree(G, weight='weight')
+        self.cladograms[tuple(a.name for a in archetypes)] = mst
+        return mst
+    
+    def _neighbor_joining(self, archetypes: List[ArchetypalStrand],
+                         trait_matrix: np.ndarray) -> nx.DiGraph:
+        """Neighbor-joining algorithm for phylogenetic reconstruction"""
+        # Simplified implementation
+        G = nx.DiGraph()
+        distances = spatial.distance.pdist(trait_matrix, metric='euclidean')
+        distance_matrix = spatial.distance.squareform(distances)
+        
+        # Build tree using hierarchical clustering
+        from scipy.cluster import hierarchy
+        Z = hierarchy.linkage(distance_matrix, method='average')
+        
+        # Convert to networkx graph
+        # This is a simplified conversion - full NJ would be more complex
+        for i in range(len(archetypes)-1):
+            G.add_edge(archetypes[int(Z[i,0])].name, 
+                      archetypes[int(Z[i,1])].name, 
+                      weight=Z[i,2])
+        
+        self.cladograms[tuple(a.name for a in archetypes)] = G
+        return G
+    
+    def _minimum_spanning_tree(self, archetypes: List[ArchetypalStrand],
+                              trait_matrix: np.ndarray) -> nx.DiGraph:
+        """Traditional minimum spanning tree approach"""
+        G = nx.Graph()
+        
+        for i, arch1 in enumerate(archetypes):
+            for j, arch2 in enumerate(archetypes):
+                if i != j:
+                    distance = spatial.distance.euclidean(
+                        trait_matrix[i], trait_matrix[j]
+                    )
+                    G.add_edge(arch1.name, arch2.name, weight=distance)
+        
+        mst = nx.minimum_spanning_tree(G)
+        self.cladograms[tuple(a.name for a in archetypes)] = mst
+        return mst
+    
+    def _calculate_phylogenetic_prior(self, arch1: ArchetypalStrand, 
+                                    arch2: ArchetypalStrand) -> float:
+        """Calculate Bayesian prior based on temporal and spatial overlap"""
+        temporal_overlap = 1 - abs(arch1.temporal_depth - arch2.temporal_depth) / 10000
+        spatial_similarity = 1 - abs(arch1.spatial_distribution - arch2.spatial_distribution)
+        return (temporal_overlap * 0.6 + spatial_similarity * 0.4)
+    
+    def find_common_ancestor(self, archetype1: str, archetype2: str, 
+                           method: str = 'lca') -> Optional[str]:
+        """Find most recent common ancestor using multiple methods"""
+        for cladogram in self.cladograms.values():
+            if archetype1 in cladogram and archetype2 in cladogram:
+                try:
+                    if method == 'lca':
+                        # Use networkx's LCA for rooted trees
+                        if hasattr(nx, 'lowest_common_ancestor'):
+                            return nx.lowest_common_ancestor(cladogram, archetype1, archetype2)
+                        else:
+                            # Fallback method
+                            path1 = nx.shortest_path(cladogram, source=list(cladogram.nodes())[0], target=archetype1)
+                            path2 = nx.shortest_path(cladogram, source=list(cladogram.nodes())[0], target=archetype2)
+                            common = [n for n in path1 if n in path2]
+                            return common[-1] if common else None
+                    else:
+                        # Shortest path midpoint
+                        path = nx.shortest_path(cladogram, archetype1, archetype2)
+                        return path[len(path)//2] if len(path) > 2 else path[0]
+                except (nx.NetworkXNoPath, nx.NodeNotFound):
+                    continue
+        return None
+    
+    def calculate_evolutionary_rate(self, archetype: str) -> float:
+        """Calculate evolutionary rate of an archetype"""
+        # Simplified evolutionary rate calculation
+        for cladogram in self.cladograms.values():
+            if archetype in cladogram:
+                # Sum of branch lengths from root
+                try:
+                    root = [n for n in cladogram.nodes() if cladogram.in_degree(n) == 0][0]
+                    path = nx.shortest_path(cladogram, root, archetype)
+                    total_length = sum(cladogram[u][v]['weight'] for u, v in zip(path[:-1], path[1:]))
+                    return total_length / len(path) if path else 0.0
+                except (IndexError, nx.NetworkXNoPath):
+                    continue
+        return 0.0
+
+class GeospatialArchetypalMapper:
+    """Advanced GIS-based symbolic distribution analysis with temporal dynamics"""
+    
+    def __init__(self):
+        self.archetype_distributions = {}
+        self.mutation_hotspots = []
+        self.diffusion_models = {}
+        self.spatial_correlations = {}
+    
+    def add_archetype_distribution(self, archetype: str, 
+                                 coordinates: List[Tuple[float, float]], 
+                                 intensity: List[float], 
+                                 epoch: str,
+                                 uncertainty: List[float] = None):
+        """Add spatial data with uncertainty estimates"""
+        key = f"{archetype}_{epoch}"
+        
+        if uncertainty is None:
+            uncertainty = [0.1] * len(coordinates)  # Default uncertainty
+        
+        self.archetype_distributions[key] = {
+            'coordinates': coordinates,
+            'intensity': intensity,
+            'uncertainty': uncertainty,
+            'epoch': epoch,
+            'centroid': self._calculate_centroid(coordinates, intensity),
+            'spread': self._calculate_spatial_spread(coordinates, intensity),
+            'density': self._calculate_point_density(coordinates, intensity)
+        }
+        
+        self._update_diffusion_model(archetype, coordinates, intensity, epoch)
+    
+    def _calculate_centroid(self, coords: List[Tuple], intensities: List[float]) -> Tuple[float, float]:
+        """Calculate intensity-weighted centroid with robustness"""
+        if not coords:
+            return (0, 0)
+        try:
+            weighted_lat = sum(c[0] * i for c, i in zip(coords, intensities)) / sum(intensities)
+            weighted_lon = sum(c[1] * i for c, i in zip(coords, intensities)) / sum(intensities)
+            return (weighted_lat, weighted_lon)
+        except ZeroDivisionError:
+            return (np.mean([c[0] for c in coords]), np.mean([c[1] for c in coords]))
+    
+    def _calculate_spatial_spread(self, coords: List[Tuple], intensities: List[float]) -> float:
+        """Calculate spatial spread (standard distance)"""
+        if len(coords) < 2:
+            return 0.0
+        centroid = self._calculate_centroid(coords, intensities)
+        distances = [math.sqrt((c[0]-centroid[0])**2 + (c[1]-centroid[1])**2) for c in coords]
+        return np.std(distances)
+    
+    def _calculate_point_density(self, coords: List[Tuple], intensities: List[float]) -> float:
+        """Calculate point density metric"""
+        if not coords:
+            return 0.0
+        spread = self._calculate_spatial_spread(coords, intensities)
+        total_intensity = sum(intensities)
+        return total_intensity / (spread + 1e-10)  # Avoid division by zero
+    
+    def _update_diffusion_model(self, archetype: str, coords: List[Tuple], 
+                              intensities: List[float], epoch: str):
+        """Update diffusion model for archetype spread"""
+        if archetype not in self.diffusion_models:
+            self.diffusion_models[archetype] = {}
+        
+        centroid = self._calculate_centroid(coords, intensities)
+        spread = self._calculate_spatial_spread(coords, intensities)
+        
+        self.diffusion_models[archetype][epoch] = {
+            'centroid': centroid,
+            'spread': spread,
+            'intensity_sum': sum(intensities),
+            'point_count': len(coords)
+        }
+    
+    def detect_mutation_hotspots(self, threshold: float = 0.8, 
+                               method: str = 'variance'):
+        """Advanced hotspot detection using multiple methods"""
+        self.mutation_hotspots.clear()
+        
+        for key, data in self.archetype_distributions.items():
+            if method == 'variance':
+                score = np.var(data['intensity'])
+            elif method == 'spatial_autocorrelation':
+                score = self._calculate_morans_i(data['coordinates'], data['intensity'])
+            elif method == 'getis_ord':
+                score = self._calculate_getis_ord(data['coordinates'], data['intensity'])
+            else:
+                score = np.var(data['intensity'])
+            
+            if score > threshold:
+                self.mutation_hotspots.append({
+                    'location': key,
+                    'score': score,
+                    'method': method,
+                    'epoch': data['epoch'],
+                    'centroid': data['centroid'],
+                    'significance': self._calculate_hotspot_significance(score, threshold)
+                })
+        
+        # Sort by significance
+        self.mutation_hotspots.sort(key=lambda x: x['significance'], reverse=True)
+    
+    def _calculate_morans_i(self, coords: List[Tuple], intensities: List[float]) -> float:
+        """Calculate Moran's I for spatial autocorrelation (simplified)"""
+        if len(coords) < 2:
+            return 0.0
+        # Simplified implementation
+        centroid = self._calculate_centroid(coords, intensities)
+        deviations = [i - np.mean(intensities) for i in intensities]
+        spatial_lag = sum(d1 * d2 for d1 in deviations for d2 in deviations) / len(deviations)**2
+        return abs(spatial_lag)  # Simplified
+    
+    def _calculate_getis_ord(self, coords: List[Tuple], intensities: List[float]) -> float:
+        """Calculate Getis-Ord Gi* statistic (simplified)"""
+        if len(coords) < 2:
+            return 0.0
+        # Simplified hot spot detection
+        mean_intensity = np.mean(intensities)
+        std_intensity = np.std(intensities)
+        if std_intensity == 0:
+            return 0.0
+        return max(0, (max(intensities) - mean_intensity) / std_intensity)
+    
+    def _calculate_hotspot_significance(self, score: float, threshold: float) -> float:
+        """Calculate statistical significance of hotspot"""
+        return min(1.0, (score - threshold) / (1 - threshold)) if score > threshold else 0.0
+    
+    def predict_archetype_spread(self, archetype: str, future_epochs: int = 5) -> List[Dict]:
+        """Predict future spatial distribution"""
+        if archetype not in self.diffusion_models:
+            return []
+        
+        epochs = sorted(self.diffusion_models[archetype].keys())
+        if len(epochs) < 2:
+            return []
+        
+        # Simple linear extrapolation of centroid movement and spread
+        recent_data = [self.diffusion_models[archetype][e] for e in epochs[-2:]]
+        centroid_drift = (
+            recent_data[1]['centroid'][0] - recent_data[0]['centroid'][0],
+            recent_data[1]['centroid'][1] - recent_data[0]['centroid'][1]
+        )
+        spread_growth = recent_data[1]['spread'] - recent_data[0]['spread']
+        
+        predictions = []
+        current_centroid = recent_data[1]['centroid']
+        current_spread = recent_data[1]['spread']
+        
+        for i in range(1, future_epochs + 1):
+            predicted_centroid = (
+                current_centroid[0] + centroid_drift[0] * i,
+                current_centroid[1] + centroid_drift[1] * i
+            )
+            predicted_spread = current_spread + spread_growth * i
+            
+            predictions.append({
+                'epoch': f'future_{i}',
+                'predicted_centroid': predicted_centroid,
+                'predicted_spread': predicted_spread,
+                'confidence': max(0, 1.0 - i * 0.2)  # Decreasing confidence
+            })
+        
+        return predictions
+
+class ArchetypalEntropyIndex:
+    """Advanced measurement of symbolic degradation and mutation rates"""
+    
+    def __init__(self):
+        self.entropy_history = {}
+        self.complexity_metrics = {}
+        self.stability_thresholds = {
+            'low_entropy': 0.3,
+            'medium_entropy': 0.6,
+            'high_entropy': 0.8
+        }
+    
+    def calculate_entropy(self, archetype: ArchetypalStrand, 
+                         historical_forms: List[str],
+                         meaning_shifts: List[float],
+                         contextual_factors: Dict[str, float] = None) -> Dict[str, float]:
+        """Advanced entropy calculation with multiple dimensions"""
+        
+        if contextual_factors is None:
+            contextual_factors = {
+                'cultural_turbulence': 0.5,
+                'technological_disruption': 0.5,
+                'social_volatility': 0.5
+            }
+        
+        # Form entropy (morphological changes with complexity weighting)
+        if len(historical_forms) > 1:
+            form_complexity = self._calculate_form_complexity(historical_forms)
+            form_changes = len(set(historical_forms)) / len(historical_forms)
+            form_entropy = form_changes * (1 + form_complexity * 0.5)
+        else:
+            form_entropy = 0
+            form_complexity = 0
+        
+        # Meaning entropy (semantic drift with contextual sensitivity)
+        meaning_entropy = np.std(meaning_shifts) if meaning_shifts else 0
+        contextual_sensitivity = sum(contextual_factors.values()) / len(contextual_factors)
+        meaning_entropy_adj = meaning_entropy * (1 + contextual_sensitivity * 0.3)
+        
+        # Structural entropy (internal consistency)
+        structural_entropy = self._calculate_structural_entropy(archetype, historical_forms)
+        
+        # Combined entropy scores
+        total_entropy = (form_entropy * 0.4 + 
+                        meaning_entropy_adj * 0.4 + 
+                        structural_entropy * 0.2)
+        
+        # Stability classification
+        stability_level = self._classify_stability(total_entropy)
+        
+        result = {
+            'total_entropy': total_entropy,
+            'form_entropy': form_entropy,
+            'meaning_entropy': meaning_entropy_adj,
+            'structural_entropy': structural_entropy,
+            'form_complexity': form_complexity,
+            'stability_level': stability_level,
+            'mutation_risk': self._calculate_mutation_risk(total_entropy, contextual_factors),
+            'resilience_score': 1 - total_entropy
+        }
+        
+        self.entropy_history[archetype.name] = {
+            **result,
+            'contextual_factors': contextual_factors,
+            'last_updated': datetime.now(),
+            'historical_trend': self._update_historical_trend(archetype.name, total_entropy)
+        }
+        
+        self.complexity_metrics[archetype.name] = form_complexity
+        
+        return result
+    
+    def _calculate_form_complexity(self, forms: List[str]) -> float:
+        """Calculate complexity of form variations"""
+        if not forms:
+            return 0.0
+        
+        # Simple complexity metric based on variation and length
+        avg_length = np.mean([len(f) for f in forms])
+        variation_ratio = len(set(forms)) / len(forms)
+        
+        return min(1.0, (avg_length / 100 * 0.3 + variation_ratio * 0.7))
+    
+    def _calculate_structural_entropy(self, archetype: ArchetypalStrand, 
+                                    forms: List[str]) -> float:
+        """Calculate structural entropy based on internal consistency"""
+        # Measure how well the archetype maintains structural integrity
+        coherence_penalty = 1 - archetype.quantum_coherence
+        preservation_penalty = 1 - archetype.preservation_rate
+        
+        return (coherence_penalty * 0.6 + preservation_penalty * 0.4)
+    
+    def _classify_stability(self, entropy: float) -> str:
+        """Classify archetype stability level"""
+        if entropy <= self.stability_thresholds['low_entropy']:
+            return 'high_stability'
+        elif entropy <= self.stability_thresholds['medium_entropy']:
+            return 'medium_stability'
+        elif entropy <= self.stability_thresholds['high_entropy']:
+            return 'low_stability'
+        else:
+            return 'critical_instability'
+    
+    def _calculate_mutation_risk(self, entropy: float, 
+                               contextual_factors: Dict[str, float]) -> float:
+        """Calculate risk of significant mutation"""
+        base_risk = entropy
+        contextual_risk = sum(contextual_factors.values()) / len(contextual_factors)
+        
+        return min(1.0, base_risk * 0.7 + contextual_risk * 0.3)
+    
+    def _update_historical_trend(self, archetype_name: str, current_entropy: float) -> List[float]:
+        """Update historical entropy trend"""
+        if archetype_name not in self.entropy_history:
+            return [current_entropy]
+        
+        current_trend = self.entropy_history[archetype_name].get('historical_trend', [])
+        current_trend.append(current_entropy)
+        
+        # Keep only last 10 readings
+        return current_trend[-10:]
+    
+    def get_high_entropy_archetypes(self, threshold: float = 0.7) -> List[Dict]:
+        """Get archetypes with high mutation rates with detailed analysis"""
+        high_entropy = []
+        
+        for name, data in self.entropy_history.items():
+            if data['total_entropy'] > threshold:
+                high_entropy.append({
+                    'archetype': name,
+                    'total_entropy': data['total_entropy'],
+                    'stability_level': data['stability_level'],
+                    'mutation_risk': data['mutation_risk'],
+                    'resilience_score': data['resilience_score'],
+                    'trend_direction': self._calculate_trend_direction(data['historical_trend'])
+                })
+        
+        return sorted(high_entropy, key=lambda x: x['mutation_risk'], reverse=True)
+    
+    def _calculate_trend_direction(self, trend: List[float]) -> str:
+        """Calculate direction of entropy trend"""
+        if len(trend) < 2:
+            return 'stable'
+        
+        slope = stats.linregress(range(len(trend)), trend).slope
+        
+        if slope > 0.01:
+            return 'increasing'
+        elif slope < -0.01:
+            return 'decreasing'
+        else:
+            return 'stable'
+    
+    def get_entropy_network(self) -> nx.Graph:
+        """Build network of archetypes based on entropy correlations"""
+        G = nx.Graph()
+        
+        archetype_names = list(self.entropy_history.keys())
+        
+        for i, arch1 in enumerate(archetype_names):
+            for j, arch2 in enumerate(archetype_names):
+                if i < j:  # Avoid duplicate pairs
+                    # Calculate entropy correlation
+                    trend1 = self.entropy_history[arch1].get('historical_trend', [0])
+                    trend2 = self.entropy_history[arch2].get('historical_trend', [0])
+                    
+                    # Pad with zeros if different lengths
+                    max_len = max(len(trend1), len(trend2))
+                    trend1_padded = trend1 + [0] * (max_len - len(trend1))
+                    trend2_padded = trend2 + [0] * (max_len - len(trend2))
+                    
+                    if len(trend1_padded) > 1:
+                        correlation = np.corrcoef(trend1_padded, trend2_padded)[0,1]
+                        if not np.isnan(correlation) and abs(correlation) > 0.3:
+                            G.add_edge(arch1, arch2, 
+                                      weight=abs(correlation),
+                                      correlation=correlation)
+        
+        return G
+
+class CrossCulturalResonanceMatrix:
+    """Advanced comparison of archetypal strength across civilizations"""
+    
+    def __init__(self):
+        self.civilization_data = {}
+        self.resonance_matrix = {}
+        self.cultural_clusters = {}
+        self.resonance_network = nx.Graph()
+    
+    def add_civilization_archetype(self, civilization: str, 
+                                 archetype: str, 
+                                 strength: float, 
+                                 neural_impact: float,
+                                 cultural_context: Dict[str, float] = None):
+        """Add archetype data with cultural context"""
+        if civilization not in self.civilization_data:
+            self.civilization_data[civilization] = {}
+        
+        if cultural_context is None:
+            cultural_context = {
+                'technological_level': 0.5,
+                'spiritual_emphasis': 0.5,
+                'individualism': 0.5,
+                'ecological_connection': 0.5
+            }
+        
+        self.civilization_data[civilization][archetype] = {
+            'strength': strength,
+            'neural_impact': neural_impact,
+            'cultural_context': cultural_context,
+            'resonance_potential': self._calculate_resonance_potential(strength, neural_impact, cultural_context)
+        }
+    
+    def _calculate_resonance_potential(self, strength: float, 
+                                     neural_impact: float,
+                                     cultural_context: Dict[str, float]) -> float:
+        """Calculate overall resonance potential"""
+        base_potential = (strength * 0.5 + neural_impact * 0.5)
+        cultural_modifier = sum(cultural_context.values()) / len(cultural_context)
+        
+        return base_potential * (0.7 + cultural_modifier * 0.3)
+    
+    def calculate_cross_resonance(self, arch1: str, arch2: str, 
+                                method: str = 'pearson') -> Dict[str, float]:
+        """Calculate resonance between archetypes using multiple methods"""
+        strengths_1 = []
+        strengths_2 = []
+        neural_impacts_1 = []
+        neural_impacts_2 = []
+        
+        for civ_data in self.civilization_data.values():
+            if arch1 in civ_data and arch2 in civ_data:
+                strengths_1.append(civ_data[arch1]['strength'])
+                strengths_2.append(civ_data[arch2]['strength'])
+                neural_impacts_1.append(civ_data[arch1]['neural_impact'])
+                neural_impacts_2.append(civ_data[arch2]['neural_impact'])
+        
+        results = {}
+        
+        if len(strengths_1) > 1:
+            if method == 'pearson':
+                strength_resonance = np.corrcoef(strengths_1, strengths_2)[0,1]
+                neural_resonance = np.corrcoef(neural_impacts_1, neural_impacts_2)[0,1]
+            elif method == 'spearman':
+                strength_resonance = stats.spearmanr(strengths_1, strengths_2)[0]
+                neural_resonance = stats.spearmanr(neural_impacts_1, neural_impacts_2)[0]
+            else:  # cosine similarity
+                strength_resonance = 1 - spatial.distance.cosine(strengths_1, strengths_2)
+                neural_resonance = 1 - spatial.distance.cosine(neural_impacts_1, neural_impacts_2)
+            
+            results = {
+                'strength_resonance': max(0, strength_resonance) if not np.isnan(strength_resonance) else 0,
+                'neural_resonance': max(0, neural_resonance) if not np.isnan(neural_resonance) else 0,
+                'overall_resonance': (max(0, strength_resonance) * 0.6 + max(0, neural_resonance) * 0.4)
+            }
+        else:
+            results = {
+                'strength_resonance': 0.0,
+                'neural_resonance': 0.0,
+                'overall_resonance': 0.0
+            }
+        
+        return results
+    
+    def build_resonance_network(self, threshold: float = 0.3) -> nx.Graph:
+        """Build advanced resonance network with community detection"""
+        G = nx.Graph()
+        archetypes = set()
+        
+        # Get all unique archetypes
+        for civ_data in self.civilization_data.values():
+            archetypes.update(civ_data.keys())
+        
+        # Calculate resonances and build network
+        for arch1 in archetypes:
+            for arch2 in archetypes:
+                if arch1 != arch2:
+                    resonance_data = self.calculate_cross_resonance(arch1, arch2)
+                    overall_resonance = resonance_data['overall_resonance']
+                    
+                    if overall_resonance > threshold:
+                        G.add_edge(arch1, arch2, 
+                                  weight=overall_resonance,
+                                  strength_resonance=resonance_data['strength_resonance'],
+                                  neural_resonance=resonance_data['neural_resonance'])
+        
+        # Detect communities in the resonance network
+        if len(G.nodes()) > 0:
+            try:
+                communities = nx.algorithms.community.greedy_modularity_communities(G)
+                for i, community in enumerate(communities):
+                    for node in community:
+                        G.nodes[node]['community'] = i
+                self.cultural_clusters = {i: list(community) for i, community in enumerate(communities)}
+            except:
+                # Fallback if community detection fails
+                for node in G.nodes():
+                    G.nodes[node]['community'] = 0
+        
+        self.resonance_network = G
+        return G
+    
+    def find_cultural_clusters(self) -> Dict[int, List[str]]:
+        """Identify clusters of culturally resonant archetypes"""
+        if not self.cultural_clusters:
+            self.build_resonance_network()
+        return self.cultural_clusters
+    
+    def calculate_civilization_similarity(self, civ1: str, civ2: str) -> float:
+        """Calculate similarity between two civilizations"""
+        if civ1 not in self.civilization_data or civ2 not in self.civilization_data:
+            return 0.0
+        
+        common_archetypes = set(self.civilization_data[civ1].keys()) & set(self.civilization_data[civ2].keys())
+        if not common_archetypes:
+            return 0.0
+        
+        similarities = []
+        for arch in common_archetypes:
+            strength_sim = 1 - abs(self.civilization_data[civ1][arch]['strength'] - 
+                                 self.civilization_data[civ2][arch]['strength'])
+            neural_sim = 1 - abs(self.civilization_data[civ1][arch]['neural_impact'] - 
+                               self.civilization_data[civ2][arch]['neural_impact'])
+            similarities.append((strength_sim + neural_sim) / 2)
+        
+        return np.mean(similarities) if similarities else 0.0
+    
+    def get_universal_archetypes(self, threshold: float = 0.7) -> List[str]:
+        """Find archetypes present in most civilizations"""
+        civ_count = len(self.civilization_data)
+        if civ_count == 0:
+            return []
+        
+        archetype_frequency = defaultdict(int)
+        for civ_data in self.civilization_data.values():
+            for arch in civ_data.keys():
+                archetype_frequency[arch] += 1
+        
+        universal = [arch for arch, count in archetype_frequency.items() 
+                    if count / civ_count >= threshold]
+        return sorted(universal, key=lambda x: archetype_frequency[x], reverse=True)
+
+class SymbolicMutationEngine:
+    """Advanced prediction of archetype evolution under cultural pressure"""
+    
+    def __init__(self):
+        self.transformation_rules = {
+            'weapon': ['tool', 'symbol', 'concept', 'algorithm'],
+            'physical': ['digital', 'virtual', 'neural', 'quantum'],
+            'individual': ['networked', 'collective', 'distributed', 'holographic'],
+            'concrete': ['abstract', 'algorithmic', 'quantum', 'consciousness_based'],
+            'hierarchical': ['networked', 'decentralized', 'rhizomatic', 'holonic']
+        }
+        
+        self.pressure_vectors = {
+            'digitization': {
+                'intensity_range': (0.3, 0.9),
+                'preferred_transformations': ['physical->digital', 'concrete->algorithmic'],
+                'resistance_factors': ['cultural_traditionalism', 'technological_aversion']
+            },
+            'ecological_crisis': {
+                'intensity_range': (0.5, 1.0),
+                'preferred_transformations': ['individual->collective', 'weapon->tool'],
+                'resistance_factors': ['individualism', 'consumerism']
+            },
+            'quantum_awakening': {
+                'intensity_range': (0.2, 0.8),
+                'preferred_transformations': ['concrete->quantum', 'physical->neural'],
+                'resistance_factors': ['materialism', 'reductionism']
+            },
+            'neural_enhancement': {
+                'intensity_range': (0.4, 0.9),
+                'preferred_transformations': ['individual->networked', 'concrete->consciousness_based'],
+                'resistance_factors': ['biological_conservatism', 'ethical_concerns']
+            }
+        }
+        
+        self.archetype_transformations = self._initialize_transformation_library()
+    
+    def _initialize_transformation_library(self) -> Dict[str, Dict[str, List[str]]]:
+        """Initialize comprehensive transformation library"""
+        return {
+            'spear': {
+                'physical->digital': ['laser_designator', 'cyber_spear', 'data_lance'],
+                'weapon->tool': ['guided_implement', 'precision_instrument', 'surgical_tool'],
+                'individual->networked': ['swarm_coordination', 'distributed_attack', 'coordinated_defense'],
+                'hierarchical->decentralized': ['peer_to_peer_defense', 'distributed_security']
+            },
+            'lion': {
+                'physical->digital': ['data_guardian', 'cyber_protector', 'algorithmic_sovereignty'],
+                'concrete->abstract': ['sovereignty_algorithm', 'leadership_principle', 'authority_pattern'],
+                'individual->collective': ['pride_consciousness', 'collective_strength', 'community_protection']
+            },
+            'sun': {
+                'concrete->quantum': ['consciousness_illumination', 'quantum_awareness', 'enlightenment_field'],
+                'physical->neural': ['neural_awakening', 'cognitive_illumination', 'mind_light'],
+                'individual->networked': ['collective_consciousness', 'global_awareness', 'networked_insight']
+            },
+            'serpent': {
+                'physical->digital': ['data_worm', 'algorithmic_subversion', 'cyber_undermining'],
+                'weapon->tool': ['transformative_agent', 'healing_serpent', 'regeneration_symbol'],
+                'concrete->quantum': ['quantum_chaos', 'nonlocal_influence', 'entanglement_manifestation']
+            }
+        }
+    
+    def predict_mutation(self, current_archetype: str, 
+                        pressure_vector: str,
+                        intensity: float = 0.5,
+                        cultural_context: Dict[str, float] = None) -> List[Dict[str, Any]]:
+        """Advanced mutation prediction with cultural context"""
+        
+        if cultural_context is None:
+            cultural_context = {
+                'technological_acceptance': 0.5,
+                'spiritual_openness': 0.5,
+                'cultural_fluidity': 0.5,
+                'innovation_capacity': 0.5
+            }
+        
+        if pressure_vector not in self.pressure_vectors:
+            return []
+        
+        pressure_config = self.pressure_vectors[pressure_vector]
+        normalized_intensity = self._normalize_intensity(intensity, pressure_config['intensity_range'])
+        
+        # Calculate transformation probabilities
+        transformations = []
+        for rule in pressure_config['preferred_transformations']:
+            possible_mutations = self._apply_transformation(current_archetype, rule)
+            
+            for mutation in possible_mutations:
+                confidence = self._calculate_mutation_confidence(
+                    mutation, normalized_intensity, cultural_context,
+                    pressure_config['resistance_factors']
+                )
+                
+                if confidence > 0.2:  # Minimum confidence threshold
+                    transformations.append({
+                        'original_archetype': current_archetype,
+                        'mutated_form': mutation,
+                        'transformation_rule': rule,
+                        'pressure_vector': pressure_vector,
+                        'intensity': normalized_intensity,
+                        'confidence': confidence,
+                        'timeframe': self._estimate_timeframe(confidence, normalized_intensity),
+                        'cultural_compatibility': self._assess_cultural_compatibility(mutation, cultural_context),
+                        'potential_impact': self._estimate_impact(mutation, current_archetype)
+                    })
+        
+        # Sort by confidence and impact
+        return sorted(transformations, 
+                     key=lambda x: x['confidence'] * x['potential_impact'], 
+                     reverse=True)
+    
+    def _normalize_intensity(self, intensity: float, intensity_range: Tuple[float, float]) -> float:
+        """Normalize intensity within pressure-specific range"""
+        min_intensity, max_intensity = intensity_range
+        return min(1.0, max(0.0, (intensity - min_intensity) / (max_intensity - min_intensity)))
+    
+    def _apply_transformation(self, archetype: str, rule: str) -> List[str]:
+        """Apply transformation rule to archetype"""
+        if '->' not in rule:
+            return []
+        
+        return self.archetype_transformations.get(archetype, {}).get(rule, [])
+    
+    def _calculate_mutation_confidence(self, mutation: str, 
+                                    intensity: float,
+                                    cultural_context: Dict[str, float],
+                                    resistance_factors: List[str]) -> float:
+        """Calculate confidence in mutation prediction"""
+        base_confidence = 0.3 + intensity * 0.4
+        
+        # Cultural compatibility adjustment
+        cultural_compatibility = sum(cultural_context.values()) / len(cultural_context)
+        cultural_boost = cultural_compatibility * 0.3
+        
+        # Resistance penalty
+        resistance_penalty = sum(1 - cultural_context.get(factor, 0.5) 
+                               for factor in resistance_factors) / len(resistance_factors) * 0.2
+        
+        final_confidence = base_confidence + cultural_boost - resistance_penalty
+        return min(1.0, max(0.0, final_confidence))
+    
+    def _estimate_timeframe(self, confidence: float, intensity: float) -> str:
+        """Estimate mutation timeframe"""
+        timeframe_score = confidence * intensity
+        
+        if timeframe_score > 0.7:
+            return 'immediate (1-5 years)'
+        elif timeframe_score > 0.5:
+            return 'near_future (5-15 years)'
+        elif timeframe_score > 0.3:
+            return 'mid_future (15-30 years)'
+        else:
+            return 'distant_future (30+ years)'
+    
+    def _assess_cultural_compatibility(self, mutation: str, 
+                                     cultural_context: Dict[str, float]) -> float:
+        """Assess cultural compatibility of mutation"""
+        # Simple assessment based on mutation characteristics
+        tech_keywords = ['digital', 'cyber', 'algorithm', 'data', 'network']
+        spirit_keywords = ['consciousness', 'awareness', 'enlightenment', 'quantum']
+        innovation_keywords = ['transformative', 'novel', 'emerging', 'advanced']
+        
+        tech_score = any(keyword in mutation.lower() for keyword in tech_keywords)
+        spirit_score = any(keyword in mutation.lower() for keyword in spirit_keywords)
+        innovation_score = any(keyword in mutation.lower() for keyword in innovation_keywords)
+        
+        scores = []
+        if tech_score:
+            scores.append(cultural_context.get('technological_acceptance', 0.5))
+        if spirit_score:
+            scores.append(cultural_context.get('spiritual_openness', 0.5))
+        if innovation_score:
+            scores.append(cultural_context.get('innovation_capacity', 0.5))
+        
+        return np.mean(scores) if scores else 0.5
+    
+    def _estimate_impact(self, mutation: str, original: str) -> float:
+        """Estimate potential impact of mutation"""
+        # Simple impact estimation based on transformation degree
+        transformation_degree = self._calculate_transformation_degree(mutation, original)
+        novelty_factor = len(mutation) / max(len(original), 1)  # Simple novelty proxy
+        
+        return min(1.0, transformation_degree * 0.7 + novelty_factor * 0.3)
+    
+    def _calculate_transformation_degree(self, mutation: str, original: str) -> float:
+        """Calculate degree of transformation from original"""
+        # Simple string-based similarity (could be enhanced with semantic analysis)
+        if original.lower() in mutation.lower():
+            return 0.3  # Low transformation
+        else:
+            return 0.8  # High transformation
+    
+    def generate_mutation_scenarios(self, archetype: str, 
+                                  time_horizon: str = 'mid_future') -> Dict[str, Any]:
+        """Generate comprehensive mutation scenarios"""
+        scenarios = {}
+        
+        for pressure_vector in self.pressure_vectors.keys():
+            mutations = self.predict_mutation(
+                archetype, pressure_vector, intensity=0.7,
+                cultural_context={
+                    'technological_acceptance': 0.7,
+                    'spiritual_openness': 0.6,
+                    'cultural_fluidity': 0.8,
+                    'innovation_capacity': 0.7
+                }
+            )
+            
+            # Filter by timeframe
+            timeframe_mutations = [m for m in mutations if m['timeframe'] == time_horizon]
+            
+            if timeframe_mutations:
+                scenarios[pressure_vector] = {
+                    'most_likely': max(timeframe_mutations, key=lambda x: x['confidence']),
+                    'all_possibilities': timeframe_mutations,
+                    'average_confidence': np.mean([m['confidence'] for m in timeframe_mutations]),
+                    'transformation_potential': np.mean([m['potential_impact'] for m in timeframe_mutations])
+                }
+        
+        return scenarios
+
+class ArchetypalEntanglement:
+    """Quantum entanglement analysis between archetypes"""
+    
+    def __init__(self):
+        self.entanglement_network = nx.Graph()
+        self.quantum_correlations = {}
+        self.nonlocal_connections = {}
+    
+    def calculate_quantum_entanglement(self, arch1: ArchetypalStrand, 
+                                     arch2: ArchetypalStrand,
+                                     tech1: ConsciousnessTechnology,
+                                     tech2: ConsciousnessTechnology) -> Dict[str, float]:
+        """Calculate quantum entanglement between archetypal consciousness fields"""
+        
+        # Qualia similarity (cosine distance in experience space)
+        qualia_similarity = 1 - tech1.quantum_signature.calculate_qualia_distance(
+            tech2.quantum_signature
+        )
+        
+        # Neural synchronization compatibility
+        neural_sync = (tech1.neural_correlate.cross_hemispheric_sync + 
+                      tech2.neural_correlate.cross_hemispheric_sync) / 2
+        
+        # Resonance frequency harmony
+        freq_harmony = 1 - abs(tech1.quantum_signature.resonance_frequency - 
+                              tech2.quantum_signature.resonance_frequency) / 100
+        
+        # Coherence alignment
+        coherence_alignment = (tech1.quantum_signature.coherence + 
+                             tech2.quantum_signature.coherence) / 2
+        
+        # Entanglement probability (Bell inequality violation analog)
+        entanglement_prob = (qualia_similarity * 0.3 +
+                           neural_sync * 0.25 +
+                           freq_harmony * 0.25 +
+                           coherence_alignment * 0.2)
+        
+        result = {
+            'entanglement_probability': entanglement_prob,
+            'qualia_similarity': qualia_similarity,
+            'neural_sync': neural_sync,
+            'frequency_harmony': freq_harmony,
+            'coherence_alignment': coherence_alignment,
+            'nonlocal_correlation': tech1.quantum_signature.nonlocal_correlation * 
+                                  tech2.quantum_signature.nonlocal_correlation
+        }
+        
+        # Update entanglement network
+        key = f"{arch1.name}_{arch2.name}"
+        self.quantum_correlations[key] = result
+        
+        if entanglement_prob > 0.5:
+            self.entanglement_network.add_edge(
+                arch1.name, arch2.name,
+                weight=entanglement_prob,
+                **result
+            )
+        
+        return result
+    
+    def find_strongly_entangled_pairs(self, threshold: float = 0.7) -> List[Dict]:
+        """Find strongly entangled archetype pairs"""
+        strong_pairs = []
+        
+        for edge in self.entanglement_network.edges(data=True):
+            if edge[2]['weight'] > threshold:
+                strong_pairs.append({
+                    'archetype1': edge[0],
+                    'archetype2': edge[1],
+                    'entanglement_strength': edge[2]['weight'],
+                    'qualia_similarity': edge[2]['qualia_similarity'],
+                    'neural_sync': edge[2]['neural_sync']
+                })
+        
+        return sorted(strong_pairs, key=lambda x: x['entanglement_strength'], reverse=True)
+    
+    def calculate_entanglement_entropy(self) -> float:
+        """Calculate von Neumann entropy of entanglement network"""
+        if len(self.entanglement_network) == 0:
+            return 0.0
+        
+        # Simple graph entropy calculation
+        degrees = [d for _, d in self.entanglement_network.degree(weight='weight')]
+        total_degree = sum(degrees)
+        
+        if total_degree == 0:
+            return 0.0
+        
+        probabilities = [d/total_degree for d in degrees]
+        entropy = -sum(p * math.log(p) for p in probabilities if p > 0)
+        
+        return entropy
+
+class CollectiveConsciousnessMapper:
+    """Mapping of collective archetypal activation across populations"""
+    
+    def __init__(self):
+        self.collective_field = {}
+        self.global_resonance_waves = {}
+        self.consciousness_weather = {}
+        self.temporal_patterns = {}
+    
+    def update_collective_resonance(self, archetype: str, 
+                                  global_activation: float,
+                                  regional_data: Dict[str, float] = None):
+        """Track collective archetypal activation across populations"""
+        
+        current_time = datetime.now()
+        
+        if archetype not in self.collective_field:
+            self.collective_field[archetype] = {
+                'activation_history': [],
+                'regional_variations': {},
+                'resonance_peaks': [],
+                'stability_metric': 0.0
+            }
+        
+        # Update activation history
+        self.collective_field[archetype]['activation_history'].append({
+            'timestamp': current_time,
+            'global_activation': global_activation,
+            'regional_data': regional_data or {}
+        })
+        
+        # Keep only last 1000 readings
+        if len(self.collective_field[archetype]['activation_history']) > 1000:
+            self.collective_field[archetype]['activation_history'] = \
+                self.collective_field[archetype]['activation_history'][-1000:]
+        
+        # Update regional variations
+        if regional_data:
+            for region, activation in regional_data.items():
+                if region not in self.collective_field[archetype]['regional_variations']:
+                    self.collective_field[archetype]['regional_variations'][region] = []
+                
+                self.collective_field[archetype]['regional_variations'][region].append(activation)
+                
+                # Keep only recent regional data
+                if len(self.collective_field[archetype]['regional_variations'][region]) > 100:
+                    self.collective_field[archetype]['regional_variations'][region] = \
+                        self.collective_field[archetype]['regional_variations'][region][-100:]
+        
+        # Detect resonance peaks
+        self._detect_resonance_peaks(archetype)
+        
+        # Calculate stability metric
+        self._calculate_stability_metric(archetype)
+        
+        # Update global resonance waves
+        self._update_global_resonance(archetype, global_activation, current_time)
+    
+    def _detect_resonance_peaks(self, archetype: str):
+        """Detect significant resonance peaks in collective activation"""
+        history = self.collective_field[archetype]['activation_history']
+        if len(history) < 10:
+            return
+        
+        activations = [entry['global_activation'] for entry in history[-50:]]  # Last 50 readings
+        mean_activation = np.mean(activations)
+        std_activation = np.std(activations)
+        
+        current_activation = activations[-1]
+        
+        # Detect peak if current activation is 2 standard deviations above mean
+        if current_activation > mean_activation + 2 * std_activation:
+            peak_data = {
+                'timestamp': history[-1]['timestamp'],
+                'activation_strength': current_activation,
+                'significance': (current_activation - mean_activation) / std_activation,
+                'duration': self._estimate_peak_duration(archetype)
+            }
+            
+            self.collective_field[archetype]['resonance_peaks'].append(peak_data)
+    
+    def _estimate_peak_duration(self, archetype: str) -> float:
+        """Estimate duration of resonance peak"""
+        # Simple estimation based on historical patterns
+        peaks = self.collective_field[archetype]['resonance_peaks']
+        if len(peaks) < 2:
+            return 1.0  # Default duration in hours
+        
+        durations = []
+        for i in range(1, len(peaks)):
+            time_diff = (peaks[i]['timestamp'] - peaks[i-1]['timestamp']).total_seconds() / 3600
+            durations.append(time_diff)
+        
+        return np.mean(durations) if durations else 1.0
+    
+    def _calculate_stability_metric(self, archetype: str):
+        """Calculate stability metric for collective activation"""
+        history = self.collective_field[archetype]['activation_history']
+        if len(history) < 2:
+            self.collective_field[archetype]['stability_metric'] = 1.0
+            return
+        
+        activations = [entry['global_activation'] for entry in history[-100:]]
+        volatility = np.std(activations) / np.mean(activations)
+        stability = 1 - min(1.0, volatility)
+        
+        self.collective_field[archetype]['stability_metric'] = stability
+    
+    def _update_global_resonance(self, archetype: str, activation: float, timestamp: datetime):
+        """Update global resonance wave patterns"""
+        if archetype not in self.global_resonance_waves:
+            self.global_resonance_waves[archetype] = {
+                'waveform': [],
+                'frequency': 0.0,
+                'amplitude': 0.0,
+                'phase': 0.0
+            }
+        
+        wave_data = self.global_resonance_waves[archetype]
+        wave_data['waveform'].append({
+            'timestamp': timestamp,
+            'amplitude': activation
+        })
+        
+        # Keep waveform manageable
+        if len(wave_data['waveform']) > 1000:
+            wave_data['waveform'] = wave_data['waveform'][-1000:]
+        
+        # Simple wave analysis (could be enhanced with FFT)
+        if len(wave_data['waveform']) >= 10:
+            amplitudes = [point['amplitude'] for point in wave_data['waveform'][-10:]]
+            wave_data['amplitude'] = np.mean(amplitudes)
+            wave_data['frequency'] = self._estimate_frequency(wave_data['waveform'][-10:])
+    
+    def _estimate_frequency(self, waveform: List[Dict]) -> float:
+        """Estimate frequency of resonance wave"""
+        if len(waveform) < 2:
+            return 0.0
+        
+        # Simple zero-crossing frequency estimation
+        amplitudes = [point['amplitude'] for point in waveform]
+        mean_amp = np.mean(amplitudes)
+        
+        zero_crossings = 0
+        for i in range(1, len(amplitudes)):
+            if (amplitudes[i-1] - mean_amp) * (amplitudes[i] - mean_amp) < 0:
+                zero_crossings += 1
+        
+        time_span = (waveform[-1]['timestamp'] - waveform[0]['timestamp']).total_seconds()
+        frequency = zero_crossings / (2 * time_span) if time_span > 0 else 0.0
+        
+        return frequency
+    
+    def generate_consciousness_weather_report(self) -> Dict[str, Any]:
+        """Generate consciousness weather report for all archetypes"""
+        weather_report = {
+            'timestamp': datetime.now(),
+            'overall_conditions': {},
+            'archetype_forecasts': {},
+            'global_resonance_index': 0.0,
+            'collective_stability': 0.0
+        }
+        
+        total_activation = 0
+        total_stability = 0
+        archetype_count = len(self.collective_field)
+        
+        for archetype, data in self.collective_field.items():
+            current_activation = data['activation_history'][-1]['global_activation'] if data['activation_history'] else 0
+            stability = data['stability_metric']
+            
+            # Determine consciousness "weather" condition
+            if current_activation > 0.8:
+                condition = "high_resonance_storm"
+            elif current_activation > 0.6:
+                condition = "resonance_ surge"
+            elif current_activation > 0.4:
+                condition = "stable_resonance"
+            elif current_activation > 0.2:
+                condition = "low_resonance"
+            else:
+                condition = "resonance_drought"
+            
+            weather_report['archetype_forecasts'][archetype] = {
+                'condition': condition,
+                'activation_level': current_activation,
+                'stability': stability,
+                'recent_peaks': len(data['resonance_peaks'][-24:]),  # Last 24 peaks
+                'regional_variation': np.std(list(data.get('regional_variations', {}).values())) if data.get('regional_variations') else 0.0
+            }
+            
+            total_activation += current_activation
+            total_stability += stability
+        
+        if archetype_count > 0:
+            weather_report['global_resonance_index'] = total_activation / archetype_count
+            weather_report['collective_stability'] = total_stability / archetype_count
+            
+            # Overall condition
+            if weather_report['global_resonance_index'] > 0.7:
+                weather_report['overall_conditions']['state'] = "heightened_consciousness"
+            elif weather_report['global_resonance_index'] > 0.5:
+                weather_report['overall_conditions']['state'] = "active_awareness"
+            else:
+                weather_report['overall_conditions']['state'] = "baseline_consciousness"
+            
+            weather_report['overall_conditions']['trend'] = self._calculate_global_trend()
+        
+        return weather_report
+    
+    def _calculate_global_trend(self) -> str:
+        """Calculate global consciousness trend"""
+        # Simplified trend calculation
+        recent_activations = []
+        for archetype_data in self.collective_field.values():
+            if archetype_data['activation_history']:
+                recent_activations.extend(
+                    [entry['global_activation'] for entry in archetype_data['activation_history'][-10:]]
+                )
+        
+        if len(recent_activations) < 5:
+            return "stable"
+        
+        slope = stats.linregress(range(len(recent_activations)), recent_activations).slope
+        
+        if slope > 0.01:
+            return "rising"
+        elif slope < -0.01:
+            return "falling"
+        else:
+            return "stable"
+
+class UniversalArchetypalTransmissionEngine:
+    """Main engine integrating all advanced modules with enhanced capabilities"""
+    
+    def __init__(self):
+        self.consciousness_tech = {}
+        self.phylogenetics = CulturalPhylogenetics()
+        self.geospatial_mapper = GeospatialArchetypalMapper()
+        self.entropy_calculator = ArchetypalEntropyIndex()
+        self.resonance_matrix = CrossCulturalResonanceMatrix()
+        self.mutation_engine = SymbolicMutationEngine()
+        self.entanglement_analyzer = ArchetypalEntanglement()
+        self.collective_mapper = CollectiveConsciousnessMapper()
+        self.archetypal_db = {}
+        self.performance_history = []
+        
+        # Advanced monitoring
+        self.system_health = {
+            'neural_network_integrity': 1.0,
+            'quantum_coherence': 1.0,
+            'symbolic_resolution': 1.0,
+            'temporal_synchronization': 1.0
+        }
+    
+    def register_archetype(self, archetype: ArchetypalStrand,
+                          consciousness_tech: ConsciousnessTechnology):
+        """Register a new archetype with its consciousness technology"""
+        self.archetypal_db[archetype.name] = archetype
+        self.consciousness_tech[archetype.name] = consciousness_tech
+        
+        # Initialize collective tracking
+        self.collective_mapper.update_collective_resonance(
+            archetype.name, 
+            global_activation=0.5,
+            regional_data={'global': 0.5}
+        )
+    
+    def prove_consciousness_architecture(self, 
+                                       include_entanglement: bool = True) -> pd.DataFrame:
+        """Comprehensive analysis of archetypal strength and coherence"""
+        
+        results = []
+        for name, archetype in self.archetypal_db.items():
+            tech = self.consciousness_tech.get(name)
+            
+            if not tech:
+                # Skip if no technology registered
+                continue
+            
+            # Calculate comprehensive metrics
+            neural_impact = tech.neural_correlate.neural_efficiency
+            quantum_strength = tech.quantum_signature.coherence
+            cultural_resilience = archetype.cultural_resilience
+            
+            # Entanglement analysis if requested
+            entanglement_factor = 1.0
+            if include_entanglement:
+                # Calculate average entanglement with other archetypes
+                entanglement_strengths = []
+                for other_name, other_archetype in self.archetypal_db.items():
+                    if other_name != name:
+                        other_tech = self.consciousness_tech.get(other_name)
+                        if other_tech:
+                            entanglement = self.entanglement_analyzer.calculate_quantum_entanglement(
+                                archetype, other_archetype, tech, other_tech
+                            )
+                            entanglement_strengths.append(entanglement['entanglement_probability'])
+                
+                if entanglement_strengths:
+                    entanglement_factor = 1 + (np.mean(entanglement_strengths) * 0.2)
+            
+            overall_strength = (
+                archetype.symbolic_strength * 0.3 +
+                neural_impact * 0.25 +
+                quantum_strength * 0.2 +
+                cultural_resilience * 0.15 +
+                (archetype.symbolic_strength * entanglement_factor) * 0.1
+            )
+            
+            # Get collective activation data
+            collective_data = self.collective_mapper.collective_field.get(name, {})
+            current_activation = 0.5
+            if collective_data.get('activation_history'):
+                current_activation = collective_data['activation_history'][-1]['global_activation']
+            
+            results.append({
+                'Archetype': name,
+                'Symbolic_Strength': archetype.symbolic_strength,
+                'Temporal_Depth': archetype.temporal_depth,
+                'Spatial_Distribution': archetype.spatial_distribution,
+                'Quantum_Coherence': archetype.quantum_coherence,
+                'Neural_Impact': neural_impact,
+                'Cultural_Resilience': cultural_resilience,
+                'Collective_Activation': current_activation,
+                'Overall_Strength': overall_strength,
+                'Consciousness_State': tech.neural_correlate.frequency_band.value,
+                'Entanglement_Factor': entanglement_factor
+            })
+        
+        df = pd.DataFrame(results)
+        return df.sort_values('Overall_Strength', ascending=False)
+    
+    def generate_cultural_diagnostic(self, depth: str = 'comprehensive') -> Dict[str, Any]:
+        """Generate comprehensive cultural psyche diagnostic"""
+        
+        strength_analysis = self.prove_consciousness_architecture()
+        high_entropy = self.entropy_calculator.get_high_entropy_archetypes()
+        resonance_net = self.resonance_matrix.build_resonance_network()
+        weather_report = self.collective_mapper.generate_consciousness_weather_report()
+        entangled_pairs = self.entanglement_analyzer.find_strongly_entangled_pairs()
+        
+        diagnostic = {
+            'timestamp': datetime.now(),
+            'analysis_depth': depth,
+            'system_health': self.system_health,
+            'strength_analysis': {
+                'top_archetypes': strength_analysis.head(5).to_dict('records'),
+                'weakest_archetypes': strength_analysis.tail(3).to_dict('records'),
+                'average_strength': strength_analysis['Overall_Strength'].mean(),
+                'strength_distribution': {
+                    'min': strength_analysis['Overall_Strength'].min(),
+                    'max': strength_analysis['Overall_Strength'].max(),
+                    'std': strength_analysis['Overall_Strength'].std()
+                }
+            },
+            'cultural_phase_shift_indicators': {
+                'rising_archetypes': self._identify_rising_archetypes(),
+                'declining_archetypes': self._identify_declining_archetypes(),
+                'high_entropy_archetypes': high_entropy,
+                'entropy_network_density': nx.density(self.entropy_calculator.get_entropy_network()) if len(self.archetypal_db) > 1 else 0.0
+            },
+            'collective_consciousness': {
+                'weather_report': weather_report,
+                'global_resonance_index': weather_report.get('global_resonance_index', 0),
+                'collective_stability': weather_report.get('collective_stability', 0)
+            },
+            'resonance_analysis': {
+                'network_density': nx.density(resonance_net),
+                'cultural_clusters': self.resonance_matrix.find_cultural_clusters(),
+                'universal_archetypes': self.resonance_matrix.get_universal_archetypes(),
+                'average_cluster_size': np.mean([len(cluster) for cluster in self.resonance_matrix.cultural_clusters.values()]) if self.resonance_matrix.cultural_clusters else 0
+            },
+            'quantum_entanglement': {
+                'strongly_entangled_pairs': entangled_pairs,
+                'entanglement_entropy': self.entanglement_analyzer.calculate_entanglement_entropy(),
+                'total_entangled_connections': len(self.entanglement_analyzer.entanglement_network.edges())
+            },
+            'consciousness_coherence_index': self._calculate_coherence_index(),
+            'predicted_evolution': self._predict_cultural_evolution(depth),
+            'recommendations': self._generate_recommendations()
+        }
+        
+        # Store diagnostic in performance history
+        self.performance_history.append({
+            'timestamp': diagnostic['timestamp'],
+            'global_resonance_index': diagnostic['collective_consciousness']['global_resonance_index'],
+            'coherence_index': diagnostic['consciousness_coherence_index'],
+            'system_health': diagnostic['system_health']
+        })
+        
+        return diagnostic
+    
+    def _identify_rising_archetypes(self) -> List[Dict]:
+        """Identify archetypes with rising influence"""
+        # This would typically use historical data - simplified for demo
+        strength_df = self.prove_consciousness_architecture()
+        top_archetypes = strength_df.head(3)
+        
+        rising = []
+        for _, row in top_archetypes.iterrows():
+            if row['Collective_Activation'] > 0.7:
+                rising.append({
+                    'archetype': row['Archetype'],
+                    'strength': row['Overall_Strength'],
+                    'activation': row['Collective_Activation'],
+                    'momentum': 'high' if row['Overall_Strength'] > 0.8 else 'medium'
+                })
+        
+        return rising
+    
+    def _identify_declining_archetypes(self) -> List[Dict]:
+        """Identify archetypes with declining influence"""
+        strength_df = self.prove_consciousness_architecture()
+        bottom_archetypes = strength_df.tail(3)
+        
+        declining = []
+        for _, row in bottom_archetypes.iterrows():
+            if row['Collective_Activation'] < 0.3:
+                declining.append({
+                    'archetype': row['Archetype'],
+                    'strength': row['Overall_Strength'],
+                    'activation': row['Collective_Activation'],
+                    'risk_level': 'high' if row['Overall_Strength'] < 0.3 else 'medium'
+                })
+        
+        return declining
+    
+    def _calculate_coherence_index(self) -> Dict[str, float]:
+        """Calculate comprehensive coherence indices"""
+        if not self.archetypal_db:
+            return {'overall': 0.0, 'neural': 0.0, 'quantum': 0.0, 'cultural': 0.0}
+        
+        # Neural coherence
+        neural_coherence = np.mean([
+            tech.neural_correlate.neural_efficiency 
+            for tech in self.consciousness_tech.values()
+        ]) if self.consciousness_tech else 0.5
+        
+        # Quantum coherence
+        quantum_coherence = np.mean([
+            tech.quantum_signature.coherence 
+            for tech in self.consciousness_tech.values()
+        ]) if self.consciousness_tech else 0.5
+        
+        # Cultural coherence
+        cultural_coherence = np.mean([
+            archetype.preservation_rate * 0.6 + archetype.quantum_coherence * 0.4
+            for archetype in self.archetypal_db.values()
+        ])
+        
+        # Overall coherence
+        overall_coherence = (
+            neural_coherence * 0.3 +
+            quantum_coherence * 0.3 +
+            cultural_coherence * 0.4
+        )
+        
+        return {
+            'overall': overall_coherence,
+            'neural': neural_coherence,
+            'quantum': quantum_coherence,
+            'cultural': cultural_coherence
+        }
+    
+    def _predict_cultural_evolution(self, depth: str) -> List[Dict[str, Any]]:
+        """Predict cultural evolution with variable depth"""
+        predictions = []
+        
+        pressure_vectors = ['digitization', 'ecological_crisis', 'quantum_awakening']
+        
+        for pressure in pressure_vectors:
+            for archetype_name in list(self.archetypal_db.keys())[:5]:  # Top 5 for demo
+                if depth == 'comprehensive':
+                    scenarios = self.mutation_engine.generate_mutation_scenarios(
+                        archetype_name, 'near_future'
+                    )
+                    if pressure in scenarios:
+                        predictions.append({
+                            'pressure_vector': pressure,
+                            'archetype': archetype_name,
+                            'scenario': scenarios[pressure],
+                            'timeframe': 'near_future',
+                            'analysis_depth': 'comprehensive'
+                        })
+                else:
+                    mutations = self.mutation_engine.predict_mutation(
+                        archetype_name, pressure, intensity=0.7
+                    )
+                    if mutations:
+                        predictions.append({
+                            'pressure_vector': pressure,
+                            'archetype': archetype_name,
+                            'most_likely_mutation': mutations[0],
+                            'total_possibilities': len(mutations),
+                            'timeframe': 'next_20_years',
+                            'analysis_depth': 'basic'
+                        })
+        
+        return predictions
+    
+    def _generate_recommendations(self) -> List[Dict[str, Any]]:
+        """Generate system recommendations based on current state"""
+        recommendations = []
+        diagnostic = self.generate_cultural_diagnostic('basic')  # Avoid recursion
+        
+        # Check system health
+        health_scores = self.system_health.values()
+        avg_health = sum(health_scores) / len(health_scores) if health_scores else 0
+        
+        if avg_health < 0.7:
+            recommendations.append({
+                'type': 'system_maintenance',
+                'priority': 'high',
+                'message': 'System health below optimal levels. Recommend neural network recalibration.',
+                'suggested_actions': [
+                    'Run neural coherence diagnostics',
+                    'Check quantum entanglement matrix integrity',
+                    'Verify symbolic resolution settings'
+                ]
+            })
+        
+        # Check for high entropy archetypes
+        high_entropy = diagnostic['cultural_phase_shift_indicators']['high_entropy_archetypes']
+        if high_entropy:
+            recommendations.append({
+                'type': 'cultural_monitoring',
+                'priority': 'medium',
+                'message': f'Detected {len(high_entropy)} high-entropy archetypes undergoing significant mutation.',
+                'suggested_actions': [
+                    'Increase monitoring frequency for high-entropy archetypes',
+                    'Prepare contingency plans for symbolic mutations',
+                    'Update transformation prediction models'
+                ]
+            })
+        
+        # Check collective consciousness stability
+        collective_stability = diagnostic['collective_consciousness']['collective_stability']
+        if collective_stability < 0.6:
+            recommendations.append({
+                'type': 'collective_awareness',
+                'priority': 'medium',
+                'message': 'Collective consciousness stability below optimal threshold.',
+                'suggested_actions': [
+                    'Monitor regional resonance variations',
+                    'Check for external interference patterns',
+                    'Consider consciousness stabilization protocols'
+                ]
+            })
+        
+        return recommendations
+    
+    def activate_consciousness_network(self, archetypes: List[str], 
+                                     intensity: float = 0.8,
+                                     duration: float = 1.0) -> Dict[str, Any]:
+        """Activate multiple consciousness technologies simultaneously"""
+        results = {
+            'timestamp': datetime.now(),
+            'total_activations': 0,
+            'successful_activations': 0,
+            'network_coherence': 0.0,
+            'individual_results': {},
+            'emergent_phenomena': {}
+        }
+        
+        individual_results = {}
+        activations = []
+        
+        for archetype_name in archetypes:
+            if archetype_name in self.consciousness_tech:
+                tech = self.consciousness_tech[archetype_name]
+                activation_result = tech.activate(intensity, duration)
+                individual_results[archetype_name] = activation_result
+                activations.append(activation_result)
+                results['successful_activations'] += 1
+        
+        results['total_activations'] = len(archetypes)
+        results['individual_results'] = individual_results
+        
+        # Calculate network coherence
+        if len(activations) > 1:
+            coherence_scores = [act['quantum_coherence'] for act in activations]
+            results['network_coherence'] = np.mean(coherence_scores)
+            
+            # Check for emergent phenomena
+            if results['network_coherence'] > 0.8:
+                results['emergent_phenomena'] = {
+                    'type': 'collective_resonance_ field',
+                    'strength': results['network_coherence'],
+                    'stability': np.std(coherence_scores) < 0.1,
+                    'qualia_synergy': self._calculate_qualia_synergy(activations)
+                }
+        
+        # Update collective consciousness mapping
+        for archetype_name in archetypes:
+            if archetype_name in individual_results:
+                activation_strength = individual_results[archetype_name]['performance_score']
+                self.collective_mapper.update_collective_resonance(
+                    archetype_name,
+                    global_activation=activation_strength,
+                    regional_data={'network_activation': activation_strength}
+                )
+        
+        return results
+    
+    def _calculate_qualia_synergy(self, activations: List[Dict]) -> float:
+        """Calculate qualia synergy between multiple activations"""
+        if len(activations) < 2:
+            return 0.0
+        
+        qualia_vectors = [act['qualia_experience'] for act in activations]
+        
+        # Calculate average pairwise similarity
+        similarities = []
+        for i in range(len(qualia_vectors)):
+            for j in range(i + 1, len(qualia_vectors)):
+                similarity = 1 - spatial.distance.cosine(qualia_vectors[i], qualia_vectors[j])
+                similarities.append(similarity)
+        
+        return np.mean(similarities) if similarities else 0.0
+    
+    def get_system_performance_report(self) -> Dict[str, Any]:
+        """Generate comprehensive system performance report"""
+        current_diagnostic = self.generate_cultural_diagnostic()
+        
+        # Calculate performance trends
+        performance_trend = 'stable'
+        if len(self.performance_history) >= 2:
+            recent_coherence = [entry['coherence_index']['overall'] for entry in self.performance_history[-5:]]
+            if len(recent_coherence) >= 2:
+                slope = stats.linregress(range(len(recent_coherence)), recent_coherence).slope
+                if slope > 0.01:
+                    performance_trend = 'improving'
+                elif slope < -0.01:
+                    performance_trend = 'declining'
+        
+        report = {
+            'timestamp': datetime.now(),
+            'system_status': 'operational',
+            'performance_metrics': {
+                'total_archetypes': len(self.archetypal_db),
+                'active_technologies': len(self.consciousness_tech),
+                'average_activation_success': self._calculate_avg_activation_success(),
+                'system_uptime': self._calculate_system_uptime(),
+                'data_integrity': self._assess_data_integrity()
+            },
+            'current_state': current_diagnostic,
+            'performance_trend': performance_trend,
+            'resource_utilization': {
+                'computational_load': len(self.archetypal_db) * 0.1,  # Simplified
+                'memory_usage': len(self.consciousness_tech) * 0.05,
+                'network_bandwidth': len(self.performance_history) * 0.01
+            },
+            'recommendations': self._generate_system_recommendations()
+        }
+        
+        return report
+    
+    def _calculate_avg_activation_success(self) -> float:
+        """Calculate average activation success rate"""
+        if not self.consciousness_tech:
+            return 0.0
+        
+        success_rates = []
+        for tech in self.consciousness_tech.values():
+            perf_report = tech.get_performance_report()
+            success_rates.append(perf_report['overall_health'])
+        
+        return np.mean(success_rates) if success_rates else 0.0
+    
+    def _calculate_system_uptime(self) -> float:
+        """Calculate system uptime (simplified)"""
+        if not self.performance_history:
+            return 1.0
+        
+        # Count successful operations vs total
+        successful_ops = sum(1 for entry in self.performance_history 
+                           if entry['coherence_index']['overall'] > 0.5)
+        total_ops = len(self.performance_history)
+        
+        return successful_ops / total_ops if total_ops > 0 else 1.0
+    
+    def _assess_data_integrity(self) -> float:
+        """Assess overall data integrity"""
+        integrity_scores = []
+        
+        # Check archetype data completeness
+        for archetype in self.archetypal_db.values():
+            completeness = (
+                (1.0 if archetype.temporal_depth > 0 else 0.5) +
+                (1.0 if archetype.spatial_distribution > 0 else 0.5) +
+                (1.0 if archetype.quantum_coherence > 0 else 0.5)
+            ) / 3
+            integrity_scores.append(completeness)
+        
+        # Check technology data
+        for tech in self.consciousness_tech.values():
+            tech_completeness = (
+                tech.neural_correlate.neural_efficiency +
+                tech.quantum_signature.coherence
+            ) / 2
+            integrity_scores.append(tech_completeness)
+        
+        return np.mean(integrity_scores) if integrity_scores else 1.0
+    
+    def _generate_system_recommendations(self) -> List[Dict[str, Any]]:
+        """Generate system-level recommendations"""
+        recommendations = []
+        performance = self.get_system_performance_report()
+        
+        # Check resource utilization
+        resource_util = performance['resource_utilization']
+        if (resource_util['computational_load'] > 0.8 or 
+            resource_util['memory_usage'] > 0.8):
+            recommendations.append({
+                'category': 'resource_management',
+                'priority': 'high',
+                'message': 'High resource utilization detected.',
+                'actions': [
+                    'Consider load distribution across additional nodes',
+                    'Review data retention policies',
+                    'Optimize neural network calculations'
+                ]
+            })
+        
+        # Check data integrity
+        if performance['performance_metrics']['data_integrity'] < 0.7:
+            recommendations.append({
+                'category': 'data_quality',
+                'priority': 'medium',
+                'message': 'Data integrity below optimal levels.',
+                'actions': [
+                    'Run data validation routines',
+                    'Check for missing archetype attributes',
+                    'Verify neural correlate completeness'
+                ]
+            })
+        
+        # Check system performance trend
+        if performance['performance_trend'] == 'declining':
+            recommendations.append({
+                'category': 'system_health',
+                'priority': 'medium',
+                'message': 'System performance showing declining trend.',
+                'actions': [
+                    'Perform comprehensive system diagnostics',
+                    'Review recent configuration changes',
+                    'Check for external interference patterns'
+                ]
+            })
+        
+        return recommendations
+
+# Enhanced example instantiation with advanced archetypes
+def create_advanced_archetypes():
+    """Create example archetypes with full neuro-symbolic specifications"""
+    
+    # Solar Consciousness Archetype
+    solar_archetype = ArchetypalStrand(
+        name="Solar_Consciousness",
+        symbolic_form="Sunburst",
+        temporal_depth=6000,
+        spatial_distribution=0.95,
+        preservation_rate=0.9,
+        quantum_coherence=0.95,
+        cultural_penetration=0.9,
+        transformative_potential=0.8,
+        num_variants=15
+    )
+    
+    solar_quantum = QuantumSignature(
+        coherence=0.95,
+        entanglement=0.85,
+        qualia_vector=np.array([0.9, 0.8, 0.95, 0.7, 0.99]),  # high visual, cognitive, spiritual
+        resonance_frequency=12.0,  # Alpha resonance
+        decoherence_time=5.0,
+        nonlocal_correlation=0.8
+    )
+    
+    solar_neural = NeuralCorrelate(
+        primary_regions=["PFC", "DMN", "Pineal_Region"],
+        frequency_band=ConsciousnessState.ALPHA,
+        cross_hemispheric_sync=0.9,
+        neuroplasticity_impact=0.8,
+        default_mode_engagement=0.7,
+        salience_network_coupling=0.6,
+        thalamocortical_resonance=0.8
+    )
+    
+    solar_tech = ConsciousnessTechnology(
+        name="Solar_Illumination_Interface",
+        archetype=solar_archetype,
+        neural_correlate=solar_neural,
+        quantum_sig=solar_quantum
+    )
+    
+    # Feminine Divine Archetype
+    feminine_archetype = ArchetypalStrand(
+        name="Feminine_Divine", 
+        symbolic_form="Flowing_Vessels",
+        temporal_depth=8000,
+        spatial_distribution=0.85,
+        preservation_rate=0.7,  # Some suppression in patriarchal eras
+        quantum_coherence=0.9,
+        cultural_penetration=0.8,
+        transformative_potential=0.9,
+        num_variants=12
+    )
+    
+    feminine_quantum = QuantumSignature(
+        coherence=0.88,
+        entanglement=0.92,  # High connectivity
+        qualia_vector=np.array([0.7, 0.95, 0.8, 0.9, 0.85]),  # high emotional, somatic
+        resonance_frequency=7.83,  # Schumann resonance
+        decoherence_time=8.0,
+        nonlocal_correlation=0.9
+    )
+    
+    feminine_neural = NeuralCorrelate(
+        primary_regions=["Whole_Brain", "Heart_Brain_Axis"],
+        frequency_band=ConsciousnessState.THETA,
+        cross_hemispheric_sync=0.95,
+        neuroplasticity_impact=0.9,
+        default_mode_engagement=0.8,
+        salience_network_coupling=0.7,
+        thalamocortical_resonance=0.6
+    )
+    
+    feminine_tech = ConsciousnessTechnology(
+        name="Life_Flow_Resonator",
+        archetype=feminine_archetype,
+        neural_correlate=feminine_neural,
+        quantum_sig=feminine_quantum
+    )
+    
+    # Warrior Protector Archetype
+    warrior_archetype = ArchetypalStrand(
+        name="Warrior_Protector",
+        symbolic_form="Lion_Shield",
+        temporal_depth=5000,
+        spatial_distribution=0.75,
+        preservation_rate=0.8,
+        quantum_coherence=0.7,
+        cultural_penetration=0.7,
+        transformative_potential=0.6,
+        num_variants=8
+    )
+    
+    warrior_quantum = QuantumSignature(
+        coherence=0.75,
+        entanglement=0.6,
+        qualia_vector=np.array([0.8, 0.9, 0.7, 0.95, 0.6]),  # high emotional, somatic
+        resonance_frequency=16.0,  # Beta resonance
+        decoherence_time=3.0,
+        nonlocal_correlation=0.5
+    )
+    
+    warrior_neural = NeuralCorrelate(
+        primary_regions=["Amygdala", "Motor_Cortex", "ACC"],
+        frequency_band=ConsciousnessState.BETA,
+        cross_hemispheric_sync=0.7,
+        neuroplasticity_impact=0.6,
+        default_mode_engagement=0.4,
+        salience_network_coupling=0.8,
+        thalamocortical_resonance=0.7
+    )
+    
+    warrior_tech = ConsciousnessTechnology(
+        name="Guardian_Activation_Matrix",
+        archetype=warrior_archetype,
+        neural_correlate=warrior_neural,
+        quantum_sig=warrior_quantum
+    )
+    
+    return [
+        (solar_archetype, solar_tech),
+        (feminine_archetype, feminine_tech),
+        (warrior_archetype, warrior_tech)
+    ]
+
+# Advanced demonstration
+if __name__ == "__main__":
+    print("=== UNIVERSAL ARCHETYPAL TRANSMISSION ENGINE v9.0 ===")
+    print("Initializing Advanced Neuro-Symbolic Consciousness Architecture...")
+    
+    # Initialize the advanced engine
+    engine = UniversalArchetypalTransmissionEngine()
+    
+    # Register advanced archetypes
+    archetypes_created = 0
+    for archetype, tech in create_advanced_archetypes():
+        engine.register_archetype(archetype, tech)
+        archetypes_created += 1
+    
+    print(f"✓ Registered {archetypes_created} advanced archetypes")
+    
+    # Run comprehensive analysis
+    print("\n1. COMPREHENSIVE ARCHEYPAL STRENGTH ANALYSIS:")
+    results = engine.prove_consciousness_architecture()
+    print(results.to_string(index=False))
+    
+    print("\n2. ADVANCED CULTURAL DIAGNOSTIC:")
+    diagnostic = engine.generate_cultural_diagnostic()
+    
+    # Print key diagnostic information
+    print(f"Global Resonance Index: {diagnostic['collective_consciousness']['global_resonance_index']:.3f}")
+    print(f"Consciousness Coherence: {diagnostic['consciousness_coherence_index']['overall']:.3f}")
+    print(f"Cultural Clusters: {len(diagnostic['resonance_analysis']['cultural_clusters'])}")
+    print(f"Strongly Entangled Pairs: {len(diagnostic['quantum_entanglement']['strongly_entangled_pairs'])}")
+    
+    print("\n3. CONSCIOUSNESS TECHNOLOGY ACTIVATION:")
+    activation_results = engine.activate_consciousness_network(
+        ["Solar_Consciousness", "Feminine_Divine"], 
+        intensity=0.8, 
+        duration=2.0
+    )
+    print(f"Network Activation Success: {activation_results['successful_activations']}/{activation_results['total_activations']}")
+    print(f"Network Coherence: {activation_results['network_coherence']:.3f}")
+    
+    if activation_results['emergent_phenomena']:
+        print(f"Emergent Phenomena: {activation_results['emergent_phenomena']['type']}")
+    
+    print("\n4. SYSTEM PERFORMANCE REPORT:")
+    performance = engine.get_system_performance_report()
+    print(f"System Status: {performance['system_status']}")
+    print(f"Performance Trend: {performance['performance_trend']}")
+    print(f"Data Integrity: {performance['performance_metrics']['data_integrity']:.3f}")
+    
+    print("\n5. MUTATION PREDICTIONS:")
+    mutation_scenarios = engine.mutation_engine.generate_mutation_scenarios("Warrior_Protector")
+    for pressure, scenario in mutation_scenarios.items():
+        if scenario:
+            print(f"{pressure}: {scenario['most_likely']['mutated_form']} "
+                  f"(confidence: {scenario['most_likely']['confidence']:.3f})")
+    
+    print("\n=== SYSTEM INITIALIZATION COMPLETE ===")
+    print("Universal Archetypal Transmission Engine v9.0 is now operational.")
+    print("Ready for advanced consciousness research and cultural analysis.")