Dynamic Elasticity Diagnostics for resilient institutions.
QuantisNexis™ QNX-DES™ v5.5.1 replaces fixed substitutability assumptions with state-responsive dynamic elasticity (ρ_t), delivering defensible Quantis Index™ scores, QuantisSentra™ risk signals, and prioritized action pathways for ports, climate agencies, universities, health systems, and enterprises.
A unified engine, not a black-box AI tool.
QNX-DES™ v5.5.1 provides the dynamic elasticity foundation (ρ_t state-responsive substitutability), while advanced layers—GTSM v3.0 event calibration, spatial QI(x,y,t) heat maps, Monte Carlo uncertainty bands, RADAR, synergy, EFQM/Baldrige benchmarking, QuantisSentra, Quantis Advisors, and Quantis Narrator—deliver transparent diagnostic intelligence at port, urban, and institutional scales.
Technical foundation — for methodology and research users
QNX Core™ is a 25-variable engine integrating BMS, ATCG, VUCA, WOWA/Orness weighting, and the master equation. The platform includes RADAR scoring, EFQM/Baldrige benchmarking, Diamond-style ESI synergy, copula dependency diagnostics, ML readiness, outcome-label workflow, and Tito-Conti advisor logic.
QNX Core™
The deterministic scoring engine. Produces the Quantis Index from normalized organizational variables and pillar logic.
QuantisSentra™
Risk and decay signal layer using critical thresholds, synergy bottlenecks, anomaly flags, and trend conditions.
Quantis Advisors™
Tito-Conti-based diagnosis, treatment, and review logic with sector-specific ideal profiles.
Quantis Narrator™
Score-safe explanation layer. It explains QNX outputs but does not invent or alter scores.
QNX v5.5.1 DES system architecture. v5.5.1 · DES Package
The public website now reflects the current production direction: QNX-DES™ v5.5.1 delivers dynamic elasticity diagnostics across dashboard, API, report, and supervised ML workflows — purpose-built for institutional and climate-sector deployment.
Input sources and assessment channels
Likert, EFQM A/B/C/D, Diamond multi-rater, RADAR inputs, cohort/history records, documents, KPIs.
Quality and structure checks
Base-result validation, numeric checks, fallback warning, and output contract checks.
Core mathematical engine
Three-stage ρ_t computation: Fe-sigmoid environmental anchor → VUCA/G_r/Ψ state modulation → ATCG genome multiplier. Monte Carlo 500-sample uncertainty envelope. Dynamic elasticity replaces fixed CES substitutability.
Approach × deployment scoring
Approach × deployment scoring with RADAR-to-variable mapping.
Maturity and award-readiness bands
EFQM/Baldrige equivalent bands, award-readiness signals, and client-facing maturity language.
Interaction and tail-risk diagnostics
Diamond ESI proxy, copula-style dependency diagnostics, joint low-tail risk, and variance guard.
Risk and decay signal layer
Decay, stagnation, critical thresholds, anomaly signals, and trend-aware risk detection.
Learning, feature, and trend support
Feature store, outcome-label workflow, readiness scoring, clustering, anomaly detection, and time-series utilities. Supervised ML activates after pilot data.
Priority actions and treatment logic
Sector-specific Tito-Conti diagnosis, treatment actions, and review cycle.
Executive explanation layer
Template report blocks and optional LLM hook with no-score-invention guardrail.
Reports, dashboard, and integration outputs
Dashboard JSON, report templates, API-ready output, and future branded PDF/HTML reports.
Start from your operating context.
QNX can present the same core engine through sector-specific language, ideal profiles, benchmarks, and advisory logic.
One engine. Multiple sector contexts.
Each module uses the same QNX Core foundation but adapts ideal profiles, thresholds, benchmarks, and advisory language for the sector context.
Climate™
Water, infrastructure, resilience, environmental agencies.
Government™
Cities, counties, state agencies, public governance.
University™
Higher education, research centers, institutional quality.
Health™
Health systems, service reliability, governance, safety.
Enterprise™
Organizations, non-profits, consulting and performance teams.
QNX-DES v5.5.1 applied to NYC urban flood governance.
This demonstration applies QNX-DES v5.5.1 dynamic elasticity scoring to the NYC urban flood governance case study, producing: Quantis Index, QuantisSentra flag, synergy bottleneck, copula tail-risk signal, Tito-Conti priorities, and client-ready report narrative.
NYC Urban Flood Governance Under Compound Flood Hazards
Applied research case study using the quantitative organizational genomics framework. Authors: Morteza Shakeri Majd, Ph.D.; Dr. Mehrdad Rastgou.
QNX pillar profile
NYC shows strong analytics and governance capacity, while infrastructure resilience/process stability is the main bottleneck under compound flood stress.
Client narrative: NYC demonstrates moderate resilience under high compound hazard stress. Strong planning and governance partially offset exposure, but resilience/process stability remains the key improvement target.
Tito-Conti priority areas
| Rank | Pillar | Actual | Target | Status | Priority |
|---|---|---|---|---|---|
| 1 | RE | 0.54 | 0.82 | WARNING | 0.58 |
| 2 | ET | 0.76 | 0.88 | STABLE | 0.12 |
| 3 | EI | 0.77 | 0.82 | STABLE | 0.05 |
| 4 | AD | 0.79 | 0.80 | STABLE | 0.01 |
| 5 | IN | 0.81 | 0.80 | STRONG | 0.00 |
Top priority: RE — strengthen flood-critical continuity pathways, cross-agency resilience drills, and capital-program triggers.
Scenario results and copula tail-risk signal
The demo uses scenario outputs from the NYC package to create a simple joint low-tail risk proxy. The resulting copula-style diagnostic flag is elevated_cohort_tail_probability.
Port Climate Resilience Diagnostics — Tuas Mega Port & Jurong Port
QNX-DES v5.5.1 applied to Singapore's two major port facilities across 14 climate scenario-season combinations from Singapore's Third National Climate Change Study (V3, CCRS/NEA 2024), calibrated with GTSM v3.0 extreme sea level return periods. The dynamic elasticity parameter ρ_t shifts from cobb-douglas (NE Monsoon, partial compensation) to governance-sensitive (SW Monsoon, weakest-link dominance) within the same year.
Dynamic Elasticity Port Climate Resilience — 14 Scenario-Season Matrix
ρ_t transitions from −0.10 (cobb-douglas) under NE Monsoon (DJ) baseline to −0.73 (governance-sensitive) under SSP5-8.5 end-century SW Monsoon (JJAS). QI_DES drops from 0.318 to 0.061 — an 80.8% decline across the 14 scenarios.
Table 2 — Three-Way QI Comparison (click row to inspect)
| Scenario | Season | Fe% | ρ_t | QI_DES | QI_CES | Regime |
|---|
Click a row in the table to inspect full details.
Two-Port Spatial QI Comparison — Tuas vs. Jurong
GTSM v3.0 Event-Level Fe_pct Calibration
Every RP10–RP100 extreme surge event at both ports falls in the governance-sensitive zone (Fe_pct_event = 18.8–33.9). Physical infrastructure is the binding constraint under every extreme event regardless of return period or SSP scenario.
Global Tide and Surge Model v3.0 (Muis et al. 2020), ERA5-forced 1985–2014, GPD fit to 99th pct peaks. 43,119 coastal stations at ~2.5 km resolution. Nearest station to Tuas: 7.7 km. Triple-validated against Singapore V3 projections and CPB statutory thresholds.
Scroll through the storm timeline.
A guided, event-by-event presentation that advances through the chronology like the NYC Floodline dashboard, with changing titles, metrics, and outputs as the index moves.
NYC Floodline-style scroll narrative
Use the slider or the navigation buttons to move through events one at a time. The visualization updates as the timeline advances.
Timeline story
Active event panel
Citywide flash flood emergency with severe borough-level impacts.
NYC Urban Flood Governance Dashboard
This interactive dashboard presents the applied research underpinning a Cyber-Physical RM–WOWA–VUCA framework for urban flood resilience diagnostics. It integrates NOAA MRMS radar-derived rainfall, FloodNet NYC sensor data, and NYC 311 service-request records across 9 storm events (2023–2025). This work targets ASCE Natural Hazards Review and demonstrates QNX Climate™ applied to compound urban flood governance.
Executive-ready intelligence. Audit-ready evidence.
QNX produces structured outputs for leadership, technical teams, and pilot partners. The same engine can support dashboard views, reports, JSON/API responses, and Narrator summaries.
Start a conversation. We will guide the rest.
The QuantisNexis team responds to all pilot, demo, API, and white-paper inquiries directly. Institutional onboarding, account setup, and subscription billing are handled through a guided intake process.
Pilot first. Scale when ready.
Start with a guided QNX pilot, then move into team and enterprise plans as usage, reporting needs, API requirements, and client workspaces grow.
Best for validation
For organizations testing QNX with one project, one use case, a guided workflow, and a client-ready demonstration report.
- Guided intake and QNX run
- Quantis Index™ and pillar profile
- QuantisSentra™ risk signal summary
- Client-ready dashboard/report export
- Implementation guidance session
For growing teams
For recurring use with saved assessments, client workspaces, report history, and structured organizational intelligence workflows.
- Client/project workspaces
- Saved reports and assessment history
- Recurring QNX scoring workflows
- Priority implementation support
- Stripe subscription billing planned
For API and governance
For institutions needing API access, SSO, custom deployment, data governance controls, and deeper onboarding support.
- API access and integration planning
- SSO and advanced authentication roadmap
- Custom hosting/deployment options
- Governance and audit-ready documentation
- Dedicated onboarding and support
QuantisNexis follows a pilot-first engagement model. Pricing is scoped to each institutional context, with subscription tiers introduced as pilot validation is completed.
QNX-DES v5.5.1 — production-ready for pilots, research validation, and partner deployments.
QNX-DES v5.5.1 Package delivers dynamic elasticity scoring, two-port spatial QI diagnostics, Monte Carlo uncertainty envelopes, and GTSM-calibrated event-level forcing. The Singapore and NYC case studies demonstrate production-grade V&V capability.