Create alternative religions module

alternative religions module

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+#!/usr/bin/env python3
+"""
+OBSERVER-ENGINE COGNITIVE ARCHITECTURE - ALTERNATIVE RELIGIONS MODULE
+Analysis of Anti-Religion, Esoteric, Gnostic, Satanic, Witchcraft and Ritual Traditions
+Complete Operational Implementation - Not Theoretical
+"""
+
+import numpy as np
+import asyncio
+import hashlib
+import json
+import secrets
+import logging
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Dict, List, Any, Optional, Tuple, Callable
+from datetime import datetime, timedelta
+import scipy.stats as stats
+from cryptography.hazmat.primitives import hashes
+from cryptography.hazmat.primitives.kdf.hkdf import HKDF
+from cryptography.hazmat.backends import default_backend
+import qiskit
+from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, transpile
+from qiskit_aer import AerSimulator
+from qiskit.algorithms import AmplificationProblem, Grover
+from qiskit.circuit.library import PhaseOracle
+from qiskit.quantum_info import Statevector, random_statevector
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModel
+import aiohttp
+import redis
+import sqlite3
+from contextlib import asynccontextmanager
+import psutil
+import gc
+import os
+import sys
+from pathlib import Path
+import uuid
+from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
+import matplotlib.pyplot as plt
+import seaborn as sns
+from wordcloud import WordCloud
+import networkx as nx
+
+# =============================================================================
+# CORE ANALYSIS FRAMEWORKS (INTEGRATED FROM PREVIOUS MODULES)
+# =============================================================================
+
+class UniversalLawPrimacy:
+    """Integrated from Ancient Religions Module"""
+    
+    def evaluate_alignment(self, element: str) -> Dict[str, Any]:
+        """Evaluate against Universal Law principles"""
+        principles = {
+            'free_will': ['choice', 'agency', 'sovereignty', 'consent'],
+            'cause_effect': ['consequence', 'responsibility', 'accountability', 'karma'],
+            'consciousness_primacy': ['awareness', 'mind', 'observer', 'experience'],
+            'interconnectedness': ['unity', 'relationship', 'wholeness', 'community'],
+            'growth_imperacy': ['evolution', 'development', 'learning', 'expansion']
+        }
+        
+        alignment_scores = {}
+        for principle, indicators in principles.items():
+            matches = sum(1 for indicator in indicators if indicator in element.lower())
+            alignment_scores[principle] = min(1.0, 0.3 + (matches * 0.1))
+        
+        overall_alignment = np.mean(list(alignment_scores.values()))
+        
+        return {
+            'universal_law_alignment': overall_alignment,
+            'principle_breakdown': alignment_scores,
+            'violations': self._detect_universal_law_violations(element)
+        }
+    
+    def _detect_universal_law_violations(self, element: str) -> List[str]:
+        """Detect violations of Universal Law"""
+        violations = []
+        violation_patterns = {
+            'coercion': ['must obey', 'forced', 'compulsory', 'required submission'],
+            'denial_of_agency': ['no choice', 'predetermined', 'fated', 'inevitable'],
+            'isolation_emphasis': ['alone', 'separate from', 'cut off', 'disconnected'],
+            'growth_restriction': ['stay as you are', 'do not seek', 'forbidden knowledge']
+        }
+        
+        for violation, patterns in violation_patterns.items():
+            if any(pattern in element.lower() for pattern in patterns):
+                violations.append(violation)
+        
+        return violations
+
+class InversionDetection:
+    """Integrated from Early Religions Module"""
+    
+    def analyze_inversion_patterns(self, element: str) -> Dict[str, Any]:
+        """Analyze for inversion patterns"""
+        inversion_patterns = {
+            'direct_to_mediated': {
+                'indicators': ['through the', 'only via', 'requires intermediary', 'mediated by'],
+                'original': "Direct access to spiritual experience",
+                'inverted': "Access mediated through external authority"
+            },
+            'experience_to_dogma': {
+                'indicators': ['doctrine says', 'creed states', 'orthodox belief', 'official teaching'],
+                'original': "Direct experiential knowing",
+                'inverted': "Dogmatic belief requirements"
+            },
+            'inclusive_to_exclusive': {
+                'indicators': ['only for', 'exclusive to', 'special class', 'chosen few'],
+                'original': "Universal spiritual potential",
+                'inverted': "Limited access or elite status"
+            }
+        }
+        
+        detected_inversions = []
+        inversion_details = {}
+        
+        for pattern_name, pattern_data in inversion_patterns.items():
+            matches = sum(1 for indicator in pattern_data['indicators'] if indicator in element.lower())
+            if matches > 0:
+                detected_inversions.append(pattern_name)
+                inversion_details[pattern_name] = {
+                    'detection_confidence': matches / len(pattern_data['indicators']),
+                    'original_state': pattern_data['original'],
+                    'inverted_state': pattern_data['inverted']
+                }
+        
+        inversion_score = len(detected_inversions) / len(inversion_patterns)
+        
+        return {
+            'inversion_score': inversion_score,
+            'detected_inversions': detected_inversions,
+            'inversion_details': inversion_details
+        }
+
+class ModernSophisticationAnalyzer:
+    """Integrated from Modern Religions Module"""
+    
+    def analyze_modern_characteristics(self, element: str, context: Dict) -> Dict[str, Any]:
+        """Analyze modern characteristics and camouflage"""
+        modern_metrics = {
+            'commercialization': {
+                'indicators': ['premium', 'exclusive', 'tiered', 'investment', 'paid', 'subscription'],
+                'weight': 0.25
+            },
+            'scientific_rationalization': {
+                'indicators': ['evidence-based', 'scientifically proven', 'research shows', 'clinical studies'],
+                'weight': 0.20
+            },
+            'technological_mediation': {
+                'indicators': ['app-based', 'digital', 'online only', 'virtual', 'algorithmic'],
+                'weight': 0.25
+            },
+            'therapeutic_reframing': {
+                'indicators': ['healing', 'therapy', 'trauma-informed', 'clinical', 'psychological'],
+                'weight': 0.15
+            },
+            'expertise_gatekeeping': {
+                'indicators': ['certified', 'licensed', 'accredited', 'professional', 'credentialed'],
+                'weight': 0.15
+            }
+        }
+        
+        sophistication_scores = {}
+        for metric_name, metric_data in modern_metrics.items():
+            matches = sum(1 for indicator in metric_data['indicators'] if indicator in element.lower())
+            sophistication_scores[metric_name] = min(1.0, matches * 0.2)
+        
+        overall_sophistication = sum(score * modern_metrics[metric]['weight'] 
+                                   for metric, score in sophistication_scores.items())
+        
+        return {
+            'modern_sophistication': overall_sophistication,
+            'sophistication_breakdown': sophistication_scores,
+            'camouflage_effectiveness': self._assess_camouflage(element),
+            'detection_difficulty': self._calculate_detection_difficulty(overall_sophistication)
+        }
+    
+    def _assess_camouflage(self, element: str) -> float:
+        """Assess how effectively inversions are camouflaged"""
+        camouflage_indicators = [
+            'progressive', 'inclusive', 'modern', 'updated', 'contemporary',
+            'for today', 'relevant', 'accessible', 'democratic'
+        ]
+        
+        matches = sum(1 for indicator in camouflage_indicators if indicator in element.lower())
+        return min(1.0, 0.3 + (matches * 0.1))
+    
+    def _calculate_detection_difficulty(self, sophistication: float) -> str:
+        """Calculate detection difficulty"""
+        if sophistication < 0.3:
+            return "LOW"
+        elif sophistication < 0.6:
+            return "MEDIUM"
+        else:
+            return "HIGH"
+
+# =============================================================================
+# ALTERNATIVE RELIGIONS CORE ENGINE
+# =============================================================================
+
+class AlternativeReligionsAnalyzer:
+    """Core analyzer for alternative religious and spiritual movements"""
+    
+    def __init__(self):
+        self.universal_law = UniversalLawPrimacy()
+        self.inversion_detector = InversionDetection()
+        self.modern_analyzer = ModernSophisticationAnalyzer()
+        self.alternative_database = self._initialize_alternative_database()
+        self.logger = self._setup_logging()
+    
+    def _setup_logging(self):
+        logger = logging.getLogger('AlternativeReligions')
+        logger.setLevel(logging.INFO)
+        return logger
+    
+    def _initialize_alternative_database(self) -> Dict[str, Any]:
+        """Initialize comprehensive alternative religions database"""
+        return {
+            'anti_religion_movements': {
+                'new_atheism': {
+                    'key_figures': ['Richard Dawkins', 'Christopher Hitchens', 'Sam Harris'],
+                    'core_tenets': [
+                        "Religious claims require empirical evidence",
+                        "Faith is not a valid path to truth", 
+                        "Religion often causes harm to society",
+                        "Science and reason as alternative frameworks"
+                    ],
+                    'inversion_analysis': "Rebellion against religious authority, but risks creating scientistic dogma"
+                },
+                'secular_humanism': {
+                    'core_principles': [
+                        "Human reason and ethics without supernaturalism",
+                        "Human flourishing as ultimate good",
+                        "Democracy, human rights, and personal freedom",
+                        "Naturalistic worldview"
+                    ],
+                    'strengths': "Focus on human agency and ethical responsibility",
+                    'weaknesses': "May lack transcendent dimension and spiritual technology"
+                },
+                'satanism': {
+                    'lavayan': {
+                        'tenets': [
+                            "Indulgence instead of abstinence",
+                            "Vital existence instead of spiritual pipe dreams",
+                            "Undefiled wisdom instead of hypocritical self-deceit",
+                            "Responsibility to the responsible instead of concern for psychic vampires"
+                        ],
+                        'analysis': "Theatrical inversion of Christianity, commercial elements prominent"
+                    },
+                    'theistic_satanism': {
+                        'characteristics': [
+                            "Actual worship of Satan as deity",
+                            "Pre-Christian influences",
+                            "Ritual magic and invocation",
+                            "Often secretive and initiatory"
+                        ],
+                        'analysis': "More genuine spiritual path, though controversial"
+                    }
+                }
+            },
+            'esoteric_occult_traditions': {
+                'hermeticism': {
+                    'key_texts': ['Corpus Hermeticum', 'Emerald Tablet'],
+                    'core_principles': [
+                        "Principle of Mentalism - All is Mind",
+                        "Principle of Correspondence - As above, so below",
+                        "Principle of Vibration - Nothing rests, everything moves",
+                        "Principle of Polarity - Everything is dual, opposites are identical in nature"
+                    ],
+                    'consciousness_tech': "Mental transmutation, spiritual alchemy"
+                },
+                'golden_dawn': {
+                    'practices': [
+                        "Ritual magic and ceremonial practices",
+                        "Qabalah study and application",
+                        "Tarot as symbolic system",
+                        "Astrological magic"
+                    ],
+                    'structure': "Grade system with initiations",
+                    'preservation_status': "Mixed - some authentic tech, some modern reconstruction"
+                },
+                'thelema': {
+                    'core_tenets': [
+                        "Do what thou wilt shall be the whole of the Law",
+                        "Love is the law, love under will", 
+                        "Every man and every woman is a star"
+                    ],
+                    'analysis': "Individual sovereignty emphasis, but potential for ego inflation"
+                }
+            },
+            'witchcraft_pagan_traditions': {
+                'wicca': {
+                    'origins': "20th century revival by Gerald Gardner",
+                    'practices': [
+                        "Seasonal rituals (Sabbats)",
+                        "Moon ceremonies (Esbats)",
+                        "Magic and spellwork",
+                        "Nature reverence"
+                    ],
+                    'consciousness_tech': "Energy work, natural cycles alignment",
+                    'authenticity': "Modern reconstruction with some authentic elements"
+                },
+                'traditional_witchcraft': {
+                    'characteristics': [
+                        "Folk magic practices",
+                        "Ancestral veneration",
+                        "Land spirits and familiar work",
+                        "Often solitary practice"
+                    ],
+                    'preservation': "Some unbroken family lines, much reconstruction"
+                },
+                'druidry': {
+                    'practices': [
+                        "Nature spirituality and reverence",
+                        "Bardic arts and storytelling",
+                        "Ogham and tree lore",
+                        "Seasonal celebrations"
+                    ],
+                    'strengths': "Strong ecological consciousness and community focus"
+                }
+            },
+            'gnostic_mystical_traditions': {
+                'classical_gnosticism': {
+                    'key_teachings': [
+                        "Divine spark within trapped in material world",
+                        "Salvation through gnosis (direct knowledge)",
+                        "Demiurge as false creator god",
+                        "Transcendence of material limitations"
+                    ],
+                    'consciousness_tech': "Direct experiential knowing, spiritual awakening"
+                },
+                'christian_mysticism': {
+                    'practices': [
+                        "Contemplative prayer",
+                        "Meditation and silence",
+                        "Divine union seeking",
+                        "Inner transformation"
+                    ],
+                    'figures': ["Meister Eckhart", "Teresa of Avila", "John of the Cross"],
+                    'preservation': "Authentic direct experience traditions within Christianity"
+                },
+                'kabbalah': {
+                    'practices': [
+                        "Tree of Life meditation",
+                        "Hebrew letter contemplation", 
+                        "Divine name invocation",
+                        "Soul rectification work"
+                    ],
+                    'consciousness_tech': "Map of consciousness, spiritual ascent practices"
+                }
+            },
+            'psychedelic_shamanic_traditions': {
+                'plant_medicine_traditions': {
+                    'examples': ["Ayahuasca ceremonies", "Peyote rituals", "Psilocybin use"],
+                    'consciousness_tech': "Direct states alteration, visionary experiences",
+                    'risks': "Lack of proper guidance, cultural appropriation"
+                },
+                'neo_shamanism': {
+                    'practices': [
+                        "Journeying and vision quests",
+                        "Drumming and ecstatic states",
+                        "Animal spirit work",
+                        "Healing ceremonies"
+                    ],
+                    'authenticity': "Mixed - some genuine tech, much modern adaptation"
+                }
+            }
+        }
+    
+    async def analyze_alternative_movement(self, movement_data: Dict, context: Dict) -> Dict[str, Any]:
+        """Comprehensive analysis of alternative religious movement"""
+        try:
+            self.logger.info(f"🔍 Analyzing alternative movement: {movement_data.get('name', 'unknown')}")
+            
+            # Extract key elements for analysis
+            analysis_elements = self._extract_analysis_elements(movement_data)
+            
+            # Perform multi-dimensional analysis
+            analysis_results = await self._perform_comprehensive_analysis(analysis_elements, context)
+            
+            # Calculate overall assessments
+            overall_assessment = self._calculate_overall_assessment(analysis_results, movement_data)
+            
+            result = {
+                'movement': movement_data,
+                'analysis_timestamp': datetime.utcnow().isoformat(),
+                'comprehensive_analysis': analysis_results,
+                'overall_assessment': overall_assessment,
+                'preservation_score': self._calculate_preservation_score(movement_data, analysis_results),
+                'rebellion_effectiveness': self._assess_rebellion_effectiveness(analysis_results),
+                'authenticity_rating': self._determine_authenticity(movement_data, analysis_results),
+                'consciousness_tech_quality': self._assess_consciousness_tech(movement_data),
+                'recovery_recommendations': self._generate_recovery_recommendations(analysis_results)
+            }
+            
+            return result
+            
+        except Exception as e:
+            self.logger.error(f"Alternative movement analysis failed: {e}")
+            return {
+                'movement': movement_data,
+                'error': str(e),
+                'analysis_timestamp': datetime.utcnow().isoformat()
+            }
+    
+    def _extract_analysis_elements(self, movement_data: Dict) -> List[str]:
+        """Extract key elements for analysis from movement data"""
+        elements = []
+        
+        # Extract from various data fields
+        fields_to_extract = [
+            'core_tenets', 'core_principles', 'key_teachings', 'practices',
+            'characteristics', 'tenets', 'principles'
+        ]
+        
+        for field in fields_to_extract:
+            if field in movement_data:
+                if isinstance(movement_data[field], list):
+                    elements.extend(movement_data[field])
+                else:
+                    elements.append(str(movement_data[field]))
+        
+        return elements
+    
+    async def _perform_comprehensive_analysis(self, elements: List[str], context: Dict) -> Dict[str, Any]:
+        """Perform comprehensive analysis on movement elements"""
+        universal_law_analyses = []
+        inversion_analyses = []
+        modern_analyses = []
+        
+        for element in elements:
+            # Universal Law analysis
+            universal_analysis = self.universal_law.evaluate_alignment(element)
+            universal_law_analyses.append(universal_analysis)
+            
+            # Inversion analysis
+            inversion_analysis = self.inversion_detector.analyze_inversion_patterns(element)
+            inversion_analyses.append(inversion_analysis)
+            
+            # Modern characteristics analysis
+            modern_analysis = self.modern_analyzer.analyze_modern_characteristics(element, context)
+            modern_analyses.append(modern_analysis)
+        
+        return {
+            'universal_law_analyses': universal_law_analyses,
+            'inversion_analyses': inversion_analyses,
+            'modern_analyses': modern_analyses,
+            'element_count': len(elements)
+        }
+    
+    def _calculate_overall_assessment(self, analysis_results: Dict, movement_data: Dict) -> Dict[str, Any]:
+        """Calculate overall movement assessment"""
+        # Average Universal Law alignment
+        universal_scores = [a['universal_law_alignment'] for a in analysis_results['universal_law_analyses']]
+        avg_universal_alignment = np.mean(universal_scores) if universal_scores else 0.5
+        
+        # Average inversion score
+        inversion_scores = [a['inversion_score'] for a in analysis_results['inversion_analyses']]
+        avg_inversion_score = np.mean(inversion_scores) if inversion_scores else 0.5
+        
+        # Average modern sophistication
+        modern_scores = [a['modern_sophistication'] for a in analysis_results['modern_analyses']]
+        avg_modern_sophistication = np.mean(modern_scores) if modern_scores else 0.5
+        
+        return {
+            'universal_law_alignment': avg_universal_alignment,
+            'inversion_resistance': 1.0 - avg_inversion_score,
+            'modern_sophistication': avg_modern_sophistication,
+            'overall_health_score': self._calculate_health_score(
+                avg_universal_alignment, avg_inversion_score, avg_modern_sophistication
+            )
+        }
+    
+    def _calculate_health_score(self, universal: float, inversion: float, modern: float) -> float:
+        """Calculate overall health score for movement"""
+        # Higher universal alignment and inversion resistance are good
+        # Moderate modern sophistication is ideal (too low = primitive, too high = over-commercialized)
+        modern_optimal = 1.0 - abs(modern - 0.5)  # Peak at 0.5
+        
+        health_score = (universal * 0.4) + ((1.0 - inversion) * 0.4) + (modern_optimal * 0.2)
+        return min(1.0, health_score)
+    
+    def _calculate_preservation_score(self, movement_data: Dict, analysis_results: Dict) -> float:
+        """Calculate how well movement preserves authentic consciousness technology"""
+        preservation_indicators = [
+            'consciousness_tech' in str(movement_data).lower(),
+            any('direct experience' in element.lower() for element in self._extract_analysis_elements(movement_data)),
+            any('meditation' in element.lower() or 'contemplation' in element.lower() 
+                for element in self._extract_analysis_elements(movement_data)),
+            movement_data.get('preservation_status', '').lower() in ['authentic', 'genuine', 'unbroken']
+        ]
+        
+        indicator_count = sum(preservation_indicators)
+        base_score = indicator_count / len(preservation_indicators)
+        
+        # Boost score if Universal Law alignment is high
+        universal_alignment = analysis_results['overall_assessment']['universal_law_alignment']
+        
+        return min(1.0, (base_score * 0.7) + (universal_alignment * 0.3))
+    
+    def _assess_rebellion_effectiveness(self, analysis_results: Dict) -> Dict[str, Any]:
+        """Assess effectiveness of rebellion against mainstream inversions"""
+        inversion_resistance = analysis_results['overall_assessment']['inversion_resistance']
+        universal_alignment = analysis_results['overall_assessment']['universal_law_alignment']
+        
+        # Effective rebellion maintains high Universal Law alignment while resisting inversions
+        rebellion_score = (inversion_resistance * 0.6) + (universal_alignment * 0.4)
+        
+        effectiveness_level = "LOW"
+        if rebellion_score > 0.7:
+            effectiveness_level = "HIGH"
+        elif rebellion_score > 0.5:
+            effectiveness_level = "MODERATE"
+        
+        return {
+            'rebellion_score': rebellion_score,
+            'effectiveness_level': effectiveness_level,
+            'success_criteria': "High inversion resistance + High Universal Law alignment"
+        }
+    
+    def _determine_authenticity(self, movement_data: Dict, analysis_results: Dict) -> Dict[str, Any]:
+        """Determine authenticity of movement's claims and practices"""
+        authenticity_factors = {
+            'historical_lineage': self._assess_historical_lineage(movement_data),
+            'consciousness_tech_presence': self._assess_tech_presence(movement_data),
+            'commercial_elements': self._assess_commercial_elements(analysis_results),
+            'universal_law_alignment': analysis_results['overall_assessment']['universal_law_alignment']
+        }
+        
+        authenticity_score = np.mean(list(authenticity_factors.values()))
+        
+        authenticity_level = "QUESTIONABLE"
+        if authenticity_score > 0.8:
+            authenticity_level = "HIGH"
+        elif authenticity_score > 0.6:
+            authenticity_level = "MODERATE"
+        
+        return {
+            'authenticity_score': authenticity_score,
+            'authenticity_level': authenticity_level,
+            'factor_breakdown': authenticity_factors
+        }
+    
+    def _assess_historical_lineage(self, movement_data: Dict) -> float:
+        """Assess historical lineage and origins"""
+        lineage_indicators = [
+            'unbroken' in str(movement_data).lower(),
+            'ancient' in str(movement_data).lower(),
+            'traditional' in str(movement_data).lower(),
+            movement_data.get('preservation_status', '') in ['authentic', 'genuine'],
+            'reconstruction' not in str(movement_data).lower()
+        ]
+        
+        return sum(lineage_indicators) / len(lineage_indicators)
+    
+    def _assess_tech_presence(self, movement_data: Dict) -> float:
+        """Assess presence of genuine consciousness technology"""
+        tech_indicators = [
+            'consciousness_tech' in str(movement_data),
+            any(keyword in str(movement_data).lower() 
+                for keyword in ['meditation', 'contemplation', 'direct experience', 'gnosis', 'awakening']),
+            'practices' in movement_data and len(movement_data['practices']) > 0
+        ]
+        
+        return sum(tech_indicators) / len(tech_indicators)
+    
+    def _assess_commercial_elements(self, analysis_results: Dict) -> float:
+        """Assess commercial elements (inverse score - higher commerce = lower authenticity)"""
+        modern_analyses = analysis_results['modern_analyses']
+        if not modern_analyses:
+            return 0.5
+        
+        commercial_scores = [analysis['sophistication_breakdown'].get('commercialization', 0) 
+                           for analysis in modern_analyses]
+        avg_commercial = np.mean(commercial_scores) if commercial_scores else 0
+        
+        # Invert for authenticity (higher commerce = lower authenticity)
+        return 1.0 - avg_commercial
+    
+    def _assess_consciousness_tech(self, movement_data: Dict) -> Dict[str, Any]:
+        """Assess quality and accessibility of consciousness technology"""
+        elements = self._extract_analysis_elements(movement_data)
+        
+        tech_quality_indicators = 0
+        accessibility_indicators = 0
+        
+        for element in elements:
+            element_lower = element.lower()
+            
+            # Quality indicators
+            if any(keyword in element_lower for keyword in ['direct experience', 'immediate knowing', 'unmediated']):
+                tech_quality_indicators += 2
+            if any(keyword in element_lower for keyword in ['meditation', 'contemplation', 'inner practice']):
+                tech_quality_indicators += 1
+            if any(keyword in element_lower for keyword in ['energy work', 'vibration', 'frequency']):
+                tech_quality_indicators += 1
+            
+            # Accessibility indicators
+            if any(keyword in element_lower for keyword in ['available to all', 'universal access', 'everyone can']):
+                accessibility_indicators += 2
+            if any(keyword in element_lower for keyword in ['simple', 'accessible', 'easy to learn']):
+                accessibility_indicators += 1
+            if 'requires' in element_lower and any(keyword in element_lower 
+                for keyword in ['initiation', 'certification', 'special training']):
+                accessibility_indicators -= 1
+        
+        max_quality = len(elements) * 2
+        max_access = len(elements) * 2
+        
+        quality_score = tech_quality_indicators / max_quality if max_quality > 0 else 0
+        access_score = max(0, accessibility_indicators) / max_access if max_access > 0 else 0
+        
+        return {
+            'tech_quality_score': min(1.0, quality_score),
+            'accessibility_score': min(1.0, access_score),
+            'overall_tech_score': (quality_score * 0.7) + (access_score * 0.3)
+        }
+    
+    def _generate_recovery_recommendations(self, analysis_results: Dict) -> List[str]:
+        """Generate recommendations for recovering authentic spiritual practice"""
+        recommendations = []
+        
+        universal_alignment = analysis_results['overall_assessment']['universal_law_alignment']
+        inversion_resistance = analysis_results['overall_assessment']['inversion_resistance']
+        modern_sophistication = analysis_results['overall_assessment']['modern_sophistication']
+        
+        if universal_alignment < 0.7:
+            recommendations.append("Increase alignment with Universal Law principles")
+        
+        if inversion_resistance < 0.6:
+            recommendations.append("Strengthen resistance to control and mediation patterns")
+        
+        if modern_sophistication > 0.7:
+            recommendations.append("Reduce over-commercialization and technological dependency")
+        elif modern_sophistication < 0.3:
+            recommendations.append("Update presentation while maintaining core authenticity")
+        
+        # Specific inversion recovery
+        inversion_analyses = analysis_results['inversion_analyses']
+        for analysis in inversion_analyses:
+            for inversion in analysis.get('detected_inversions', []):
+                if inversion == 'direct_to_mediated':
+                    recommendations.append("Re-emphasize direct spiritual experience over mediated access")
+                elif inversion == 'experience_to_dogma':
+                    recommendations.append("Prioritize experiential knowing over doctrinal belief")
+                elif inversion == 'inclusive_to_exclusive':
+                    recommendations.append("Ensure spiritual access remains universally available")
+        
+        return list(set(recommendations))  # Remove duplicates
+
+# =============================================================================
+# SPECIALIZED ANALYZERS FOR ALTERNATIVE CATEGORIES
+# =============================================================================
+
+class AntiReligionAnalyzer:
+    """Specialized analyzer for anti-religion movements"""
+    
+    def analyze_anti_religion_movement(self, movement_data: Dict) -> Dict[str, Any]:
+        """Analyze anti-religion movement specifically"""
+        analysis = {
+            'rebellion_type': self._determine_rebellion_type(movement_data),
+            'scientific_dogma_risk': self._assess_scientism_risk(movement_data),
+            'constructive_elements': self._identify_constructive_elements(movement_data),
+            'destructive_tendencies': self._identify_destructive_tendencies(movement_data)
+        }
+        
+        return analysis
+    
+    def _determine_rebellion_type(self, movement_data: Dict) -> str:
+        """Determine the type of rebellion exhibited"""
+        elements = self._extract_elements(movement_data)
+        
+        scientific_focus = any('science' in element.lower() or 'evidence' in element.lower() 
+                              for element in elements)
+        ethical_focus = any('ethics' in element.lower() or 'human rights' in element.lower() 
+                           for element in elements)
+        experiential_focus = any('experience' in element.lower() or 'direct' in element.lower() 
+                                for element in elements)
+        
+        if scientific_focus and not ethical_focus:
+            return "SCIENTIFIC_REBELLION"
+        elif ethical_focus and not scientific_focus:
+            return "ETHICAL_REBELLION" 
+        elif experiential_focus:
+            return "EXPERIENTIAL_REBELLION"
+        else:
+            return "GENERAL_REBELLION"
+    
+    def _assess_scientism_risk(self, movement_data: Dict) -> float:
+        """Assess risk of falling into scientism (science as dogma)"""
+        elements = self._extract_elements(movement_data)
+        
+        scientism_indicators = [
+            any('only science' in element.lower() for element in elements),
+            any('scientific method only' in element.lower() for element in elements),
+            any('empirical evidence required' in element.lower() for element in elements),
+            any('non-scientific knowledge invalid' in element.lower() for element in elements)
+        ]
+        
+        return sum(scientism_indicators) / len(scientism_indicators)
+    
+    def _identify_constructive_elements(self, movement_data: Dict) -> List[str]:
+        """Identify constructive criticism of religious institutions"""
+        constructive_elements = []
+        elements = self._extract_elements(movement_data)
+        
+        for element in elements:
+            if any(keyword in element.lower() for keyword in ['critical thinking', 'reason', 'evidence']):
+                constructive_elements.append("Promotes critical examination")
+            if any(keyword in element.lower() for keyword in ['human rights', 'ethics', 'compassion']):
+                constructive_elements.append("Emphasizes ethical considerations")
+            if any(keyword in element.lower() for keyword in ['personal freedom', 'individual choice']):
+                constructive_elements.append("Supports individual sovereignty")
+        
+        return list(set(constructive_elements))
+    
+    def _identify_destructive_tendencies(self, movement_data: Dict) -> List[str]:
+        """Identify potentially destructive tendencies"""
+        destructive_tendencies = []
+        elements = self._extract_elements(movement_data)
+        
+        for element in elements:
+            if any(keyword in element.lower() for keyword in ['all religion', 'always harmful', 'inherently bad']):
+                destructive_tendencies.append("Over-generalization about religion")
+            if any(keyword in element.lower() for keyword in ['no spiritual dimension', 'consciousness illusion']):
+                destructive_tendencies.append("Denial of spiritual/consciousness reality")
+            if any(keyword in element.lower() for keyword in ['mockery', 'ridicule', 'belittle']):
+                destructive_tendencies.append("Dismissive attitude toward believers")
+        
+        return list(set(destructive_tendencies))
+    
+    def _extract_elements(self, movement_data: Dict) -> List[str]:
+        """Extract analysis elements from movement data"""
+        elements = []
+        for key in ['core_tenets', 'principles', 'teachings']:
+            if key in movement_data and isinstance(movement_data[key], list):
+                elements.extend(movement_data[key])
+        return elements
+
+class EsotericTraditionsAnalyzer:
+    """Specialized analyzer for esoteric and occult traditions"""
+    
+    def analyze_esoteric_tradition(self, movement_data: Dict) -> Dict[str, Any]:
+        """Analyze esoteric/occult tradition specifically"""
+        analysis = {
+            'initiatory_structure': self._assess_initiatory_elements(movement_data),
+            'symbolic_system_complexity': self._assess_symbolic_complexity(movement_data),
+            'practical_application': self._assess_practical_elements(movement_data),
+            'preservation_status': self._determine_preservation_status(movement_data)
+        }
+        
+        return analysis
+    
+    def _assess_initiatory_elements(self, movement_data: Dict) -> Dict[str, Any]:
+        """Assess initiatory structure and potential gatekeeping"""
+        elements = self._extract_elements(movement_data)
+        
+        initiatory_indicators = [
+            any('grade' in element.lower() for element in elements),
+            any('initiation' in element.lower() for element in elements),
+            any('degree' in element.lower() for element in elements),
+            any('hierarchy' in element.lower() for element in elements)
+        ]
+        
+        initiatory_present = any(initiatory_indicators)
+        gatekeeping_risk = sum(initiatory_indicators) / len(initiatory_indicators)
+        
+        return {
+            'initiatory_structure_present': initiatory_present,
+            'gatekeeping_risk': gatekeeping_risk,
+            'assessment': "Moderate risk" if gatekeeping_risk > 0.5 else "Low risk"
+        }
+    
+    def _assess_symbolic_complexity(self, movement_data: Dict) -> float:
+        """Assess complexity of symbolic system"""
+        complexity_indicators = [
+            'qabalah' in str(movement_data).lower(),
+            'tarot' in str(movement_data).lower(),
+            'astrology' in str(movement_data).lower(),
+            'numerology' in str(movement_data).lower(),
+            'correspondences' in str(movement_data).lower()
+        ]
+        
+        return sum(complexity_indicators) / len(complexity_indicators)
+    
+    def _assess_practical_elements(self, movement_data: Dict) -> Dict[str, Any]:
+        """Assess practical application vs theoretical focus"""
+        elements = self._extract_elements(movement_data)
+        
+        practical_indicators = [
+            any('ritual' in element.lower() for element in elements),
+            any('practice' in element.lower() for element in elements),
+            any('exercise' in element.lower() for element in elements),
+            any('meditation' in element.lower() for element in elements)
+        ]
+        
+        theoretical_indicators = [
+            any('theory' in element.lower() for element in elements),
+            any('philosophy' in element.lower() for element in elements),
+            any('doctrine' in element.lower() for element in elements),
+            any('speculation' in element.lower() for element in elements)
+        ]
+        
+        practical_score = sum(practical_indicators) / len(practical_indicators)
+        theoretical_score = sum(theoretical_indicators) / len(theoretical_indicators)
+        
+        return {
+            'practical_focus': practical_score,
+            'theoretical_focus': theoretical_score,
+            'balance_ratio': practical_score / theoretical_score if theoretical_score > 0 else float('inf')
+        }
+    
+    def _determine_preservation_status(self, movement_data: Dict) -> str:
+        """Determine preservation status of esoteric knowledge"""
+        status_indicators = {
+            'high_preservation': [
+                'unbroken lineage' in str(movement_data).lower(),
+                'authentic transmission' in str(movement_data).lower(),
+                'direct descent' in str(movement_data).lower()
+            ],
+            'moderate_preservation': [
+                'reconstruction' in str(movement_data).lower(),
+                'revival' in str(movement_data).lower(),
+                'based on' in str(movement_data).lower()
+            ],
+            'low_preservation': [
+                'modern creation' in str(movement_data).lower(),
+                'new system' in str(movement_data).lower(),
+                'invented' in str(movement_data).lower()
+            ]
+        }
+        
+        for level, indicators in status_indicators.items():
+            if any(indicator in str(movement_data).lower() for indicator in indicators):
+                return level.upper()
+        
+        return "UNKNOWN"
+    
+    def _extract_elements(self, movement_data: Dict) -> List[str]:
+        """Extract analysis elements"""
+        elements = []
+        for key in ['practices', 'teachings', 'principles']:
+            if key in movement_data and isinstance(movement_data[key], list):
+                elements.extend(movement_data[key])
+        return elements
+
+# =============================================================================
+# MAIN ALTERNATIVE RELIGIONS ENGINE
+# =============================================================================
+
+class AlternativeReligionsEngine:
+    """
+    Main engine for analyzing alternative religious and spiritual movements
+    Operational implementation - not theoretical
+    """
+    
+    def __init__(self):
+        self.core_analyzer = AlternativeReligionsAnalyzer()
+        self.anti_religion_analyzer = AntiReligionAnalyzer()
+        self.esoteric_analyzer = EsotericTraditionsAnalyzer()
+        self.analysis_history = []
+        self.logger = self._setup_logging()
+    
+    def _setup_logging(self):
+        logger = logging.getLogger('AlternativeReligionsEngine')
+        logger.setLevel(logging.INFO)
+        
+        ch = logging.StreamHandler()
+        formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+        ch.setFormatter(formatter)
+        logger.addHandler(ch)
+        
+        return logger
+
+    async def analyze_movement(self, movement_category: str, movement_name: str) -> Dict[str, Any]:
+        """
+        Analyze specific alternative movement
+        """
+        self.logger.info(f"🔮 ANALYZING ALTERNATIVE MOVEMENT: {movement_category}/{movement_name}")
+        
+        try:
+            # Get movement data from database
+            movement_data = self._get_movement_data(movement_category, movement_name)
+            if not movement_data:
+                return {'error': f"Movement {movement_name} not found in category {movement_category}"}
+            
+            # Add movement identification
+            movement_data['name'] = movement_name
+            movement_data['category'] = movement_category
+            
+            # Context for analysis
+            context = {
+                'category': movement_category,
+                'year': self._estimate_movement_year(movement_data),
+                'analysis_type': 'alternative_religions'
+            }
+            
+            # Perform core analysis
+            core_analysis = await self.core_analyzer.analyze_alternative_movement(movement_data, context)
+            
+            # Perform specialized analysis based on category
+            specialized_analysis = {}
+            if movement_category == 'anti_religion_movements':
+                specialized_analysis = self.anti_religion_analyzer.analyze_anti_religion_movement(movement_data)
+            elif movement_category in ['esoteric_occult_traditions', 'witchcraft_pagan_traditions']:
+                specialized_analysis = self.esoteric_analyzer.analyze_esoteric_tradition(movement_data)
+            
+            # Compile final results
+            result = {
+                'movement_identification': {
+                    'name': movement_name,
+                    'category': movement_category,
+                    'analysis_timestamp': datetime.utcnow().isoformat()
+                },
+                'core_analysis': core_analysis,
+                'specialized_analysis': specialized_analysis,
+                'overall_assessment': self._compile_overall_assessment(core_analysis, specialized_analysis),
+                'recommendations': self._generate_comprehensive_recommendations(core_analysis, specialized_analysis)
+            }
+            
+            # Store in history
+            self.analysis_history.append(result)
+            
+            self.logger.info(f"✅ Alternative movement analysis complete: {movement_name}")
+            
+            return result
+            
+        except Exception as e:
+            self.logger.error(f"Movement analysis failed: {e}")
+            return {
+                'movement': f"{movement_category}/{movement_name}",
+                'error': str(e),
+                'analysis_timestamp': datetime.utcnow().isoformat()
+            }
+    
+    def _get_movement_data(self, category: str, movement_name: str) -> Dict[str, Any]:
+        """Get movement data from database"""
+        database = self.core_analyzer.alternative_database
+        
+        if category in database and movement_name in database[category]:
+            return database[category][movement_name]
+        
+        # Search through subcategories
+        for main_category, subcategories in database.items():
+            if isinstance(subcategories, dict):
+                for subcategory, movements in subcategories.items():
+                    if isinstance(movements, dict) and movement_name in movements:
+                        return movements[movement_name]
+        
+        return None
+    
+    def _estimate_movement_year(self, movement_data: Dict) -> int:
+        """Estimate movement year for context"""
+        # This would be more sophisticated in production
+        year_estimates = {
+            'new_atheism': 2000,
+            'lavayan': 1966,
+            'hermeticism': -100,
+            'golden_dawn': 1888,
+            'thelema': 1904,
+            'wicca': 1950,
+            'classical_gnosticism': 100
+        }
+        
+        movement_name = movement_data.get('name', '').lower()
+        for name, year in year_estimates.items():
+            if name in movement_name:
+                return year
+        
+        return 1900  # Default
+    
+    def _compile_overall_assessment(self, core_analysis: Dict, specialized_analysis: Dict) -> Dict[str, Any]:
+        """Compile overall assessment from all analyses"""
+        core_assessment = core_analysis.get('overall_assessment', {})
+        authenticity = core_analysis.get('authenticity_rating', {})
+        tech_assessment = core_analysis.get('consciousness_tech_quality', {})
+        
+        overall_score = (
+            core_assessment.get('overall_health_score', 0.5) * 0.4 +
+            authenticity.get('authenticity_score', 0.5) * 0.3 +
+            tech_assessment.get('overall_tech_score', 0.5) * 0.3
+        )
+        
+        return {
+            'overall_score': overall_score,
+            'health_level': self._categorize_level(core_assessment.get('overall_health_score', 0.5)),
+            'authenticity_level': authenticity.get('authenticity_level', 'UNKNOWN'),
+            'tech_quality_level': self._categorize_level(tech_assessment.get('overall_tech_score', 0.5)),
+            'rebellion_effectiveness': core_analysis.get('rebellion_effectiveness', {}).get('effectiveness_level', 'UNKNOWN')
+        }
+    
+    def _categorize_level(self, score: float) -> str:
+        """Categorize score into level"""
+        if score > 0.8:
+            return "EXCELLENT"
+        elif score > 0.7:
+            return "GOOD"
+        elif score > 0.6:
+            return "FAIR"
+        elif score > 0.5:
+            return "MARGINAL"
+        else:
+            return "POOR"
+    
+    def _generate_comprehensive_recommendations(self, core_analysis: Dict, specialized_analysis: Dict) -> List[str]:
+        """Generate comprehensive recommendations"""
+        recommendations = []
+        
+        # Core recommendations
+        if 'recovery_recommendations' in core_analysis:
+            recommendations.extend(core_analysis['recovery_recommendations'])
+        
+        # Specialized recommendations based on analysis
+        if 'rebellion_type' in specialized_analysis:
+            rebellion_type = specialized_analysis['rebellion_type']
+            if rebellion_type == "SCIENTIFIC_REBELLION":
+                recommendations.append("Balance scientific emphasis with recognition of non-empirical ways of knowing")
+            elif rebellion_type == "ETHICAL_REBELLION":
+                recommendations.append("Maintain ethical focus while allowing for spiritual dimensions")
+        
+        if 'scientific_dogma_risk' in specialized_analysis:
+            risk = specialized_analysis['scientific_dogma_risk']
+            if risk > 0.5:
+                recommendations.append("Guard against scientism - science as one valid way of knowing among others")
+        
+        return list(set(recommendations))
+    
+    async def analyze_category(self, category: str) -> Dict[str, Any]:
+        """
+        Analyze entire category of alternative movements
+        """
+        self.logger.info(f"📊 ANALYZING ALTERNATIVE CATEGORY: {category}")
+        
+        try:
+            database = self.core_analyzer.alternative_database
+            if category not in database:
+                return {'error': f"Category {category} not found"}
+            
+            movement_analyses = []
+            for movement_name, movement_data in database[category].items():
+                if isinstance(movement_data, dict):  # Ensure it's a movement, not a subcategory
+                    analysis = await self.analyze_movement(category, movement_name)
+                    movement_analyses.append(analysis)
+            
+            # Calculate category metrics
+            category_metrics = self._calculate_category_metrics(movement_analyses)
+            
+            return {
+                'category': category,
+                'movement_analyses': movement_analyses,
+                'category_metrics': category_metrics,
+                'analysis_timestamp': datetime.utcnow().isoformat()
+            }
+            
+        except Exception as e:
+            self.logger.error(f"Category analysis failed: {e}")
+            return {'error': str(e)}
+    
+    def _calculate_category_metrics(self, movement_analyses: List[Dict]) -> Dict[str, Any]:
+        """Calculate metrics for entire category"""
+        if not movement_analyses:
+            return {}
+        
+        scores = []
+        authenticity_scores = []
+        tech_scores = []
+        
+        for analysis in movement_analyses:
+            if 'overall_assessment' in analysis:
+                overall = analysis['overall_assessment']
+                scores.append(overall.get('overall_score', 0.5))
+                authenticity_scores.append(overall.get('authenticity_score', 0.5))
+                tech_scores.append(overall.get('tech_score', 0.5))
+        
+        return {
+            'average_score': np.mean(scores) if scores else 0.5,
+            'average_authenticity': np.mean(authenticity_scores) if authenticity_scores else 0.5,
+            'average_tech_quality': np.mean(tech_scores) if tech_scores else 0.5,
+            'movement_count': len(movement_analyses),
+            'top_movements': sorted(
+                [(a['movement_identification']['name'], a['overall_assessment']['overall_score']) 
+                 for a in movement_analyses if 'overall_assessment' in a],
+                key=lambda x: x[1],
+                reverse=True
+            )[:3]
+        }
+    
+    def get_engine_metrics(self) -> Dict[str, Any]:
+        """Get engine performance and usage metrics"""
+        return {
+            'total_analyses': len(self.analysis_history),
+            'categories_analyzed': len(set(
+                a['movement_identification']['category'] for a in self.analysis_history
+            )),
+            'movements_analyzed': len(set(
+                a['movement_identification']['name'] for a in self.analysis_history
+            )),
+            'average_authenticity_score': np.mean([
+                a['overall_assessment']['authenticity_score'] 
+                for a in self.analysis_history 
+                if 'overall_assessment' in a
+            ]) if self.analysis_history else 0,
+            'engine_uptime': 'operational',
+            'last_analysis': self.analysis_history[-1]['movement_identification']['analysis_timestamp'] 
+                           if self.analysis_history else 'none'
+        }
+
+# =============================================================================
+# OPERATIONAL EXECUTION
+# =============================================================================
+
+async def operational_analysis():
+    """
+    Operational analysis of alternative religions - not demonstration
+    """
+    print("🌌 OBSERVER-ENGINE ALTERNATIVE RELIGIONS MODULE - OPERATIONAL")
+    print("Anti-Religion, Esoteric, Gnostic, Satanic, Witchcraft Analysis")
+    print("=" * 80)
+    
+    engine = AlternativeReligionsEngine()
+    
+    # Operational analysis tasks
+    analysis_tasks = [
+        # Anti-religion movements
+        ('anti_religion_movements', 'new_atheism'),
+        ('anti_religion_movements', 'satanism'),
+        
+        # Esoteric traditions
+        ('esoteric_occult_traditions', 'hermeticism'),
+        ('esoteric_occult_traditions', 'thelema'),
+        
+        # Witchcraft & Pagan
+        ('witchcraft_pagan_traditions', 'wicca'),
+        
+        # Gnostic traditions
+        ('gnostic_mystical_traditions', 'classical_gnosticism'),
+    ]
+    
+    results = []
+    
+    print(f"\n🎯 PERFORMING OPERATIONAL ANALYSIS OF {len(analysis_tasks)} MOVEMENTS...")
+    
+    for category, movement in analysis_tasks:
+        print(f"\n" + "="*60)
+        print(f"ANALYZING: {category.upper()} -> {movement.upper()}")
+        print("="*60)
+        
+        result = await engine.analyze_movement(category, movement)
+        results.append(result)
+        
+        if 'error' in result:
+            print(f"❌ ANALYSIS FAILED: {result['error']}")
+            continue
+        
+        # Display operational results
+        assessment = result['overall_assessment']
+        movement_id = result['movement_identification']
+        
+        print(f"📊 OPERATIONAL ASSESSMENT:")
+        print(f"   Overall Score: {assessment['overall_score']:.3f}")
+        print(f"   Health Level: {assessment['health_level']}")
+        print(f"   Authenticity: {assessment['authenticity_level']}")
+        print(f"   Tech Quality: {assessment['tech_quality_level']}")
+        print(f"   Rebellion Effectiveness: {assessment['rebellion_effectiveness']}")
+        
+        # Key findings
+        core = result['core_analysis']
+        if 'preservation_score' in core:
+            print(f"   Preservation Score: {core['preservation_score']:.3f}")
+        
+        if result['recommendations']:
+            print(f"   Key Recommendation: {result['recommendations'][0]}")
+    
+    # Category analysis
+    print("\n" + "="*80)
+    print("📈 CATEGORY-LEVEL ANALYSIS")
+    print("="*80)
+    
+    categories = ['anti_religion_movements', 'esoteric_occult_traditions']
+    for category in categories:
+        print(f"\nAnalyzing category: {category}")
+        category_result = await engine.analyze_category(category)
+        
+        if 'error' not in category_result:
+            metrics = category_result['category_metrics']
+            print(f"   Average Score: {metrics['average_score']:.3f}")
+            print(f"   Authenticity: {metrics['average_authenticity']:.3f}")
+            print(f"   Tech Quality: {metrics['average_tech_quality']:.3f}")
+            print(f"   Top Movements: {[m[0] for m in metrics['top_movements']]}")
+    
+    # Engine metrics
+    print("\n" + "="*80)
+    print("🔧 ENGINE OPERATIONAL METRICS")
+    print("="*80)
+    
+    metrics = engine.get_engine_metrics()
+    for key, value in metrics.items():
+        print(f"{key}: {value}")
+    
+    return results, metrics
+
+async def main():
+    """
+    Main operational execution
+    """
+    try:
+        print("🚀 INITIALIZING ALTERNATIVE RELIGIONS MODULE - OPERATIONAL MODE")
+        print("Anti-Religion + Esoteric + Gnostic + Satanic + Witchcraft Analysis")
+        print("Universal Law Primacy + Inversion Detection + Authenticity Verification")
+        print()
+        
+        results, metrics = await operational_analysis()
+        
+        print("\n" + "="*80)
+        print("✅ ALTERNATIVE RELIGIONS MODULE - OPERATIONAL STATUS CONFIRMED")
+        print("="*80)
+        print(f"Operational analyses completed: {metrics['total_analyses']}")
+        print(f"Movements analyzed: {metrics['movements_analyzed']}")
+        print(f"Categories covered: {metrics['categories_analyzed']}")
+        print(f"Average authenticity score: {metrics['average_authenticity_score']:.3f}")
+        print(f"Engine status: {metrics['engine_uptime']}")
+        print("\n🌌 ALTERNATIVE RELIGIONS ANALYSIS SYSTEM: FULLY OPERATIONAL")
+        
+    except Exception as e:
+        print(f"❌ Operational execution failed: {e}")
+        import traceback
+        traceback.print_exc()
+
+if __name__ == "__main__":
+    # Operational logging configuration
+    logging.basicConfig(
+        level=logging.INFO,
+        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+        handlers=[
+            logging.StreamHandler(),
+            logging.FileHandler('alternative_religions_operational.log')
+        ]
+    )
+    
+    # Execute operational analysis
+    asyncio.run(main())