Rigorous models.
Actionable insight.
Arura Analytics applies machine learning, computational modeling, and quantitative research to complex questions in transportation, infrastructure, and public-sector decision-making.
Get in touch See our workMachine Learning & AI
Predictive and statistical models built for real-world systems, from data preparation through validation.
Infrastructure Analytics
Data-driven assessment of transportation networks, structural health, and urban systems.
Policy Analysis
Quantitative research and scenario evaluation that supports evidence-based planning.
Decision Support
Clear visualization and communication that translates complex data into action.
Consulting built on research-grade rigor
Our dedicated team is ready to assist with any inquiries and ensure your consulting needs are met with precision.
Arura Analytics was founded on the belief that the methods behind strong research — careful modeling, honest validation, and transparent communication — belong in every consulting engagement. We work across technical and non-technical audiences, bridging advanced mathematics with the practical realities of planning, policy, and operations.
Methods — Machine Learning · Computational Modeling · Statistics
Deliverables — Analysis · Evaluation · Decision Support
Arura Analytics translates complex, real-world data into rigorous, decision-ready analysis for government agencies and private organizations. We build and validate the models, tools, and technical reports our clients need to make high-stakes decisions with confidence — applying peer-reviewed methods and academic-grade rigor across whatever domain the problem demands, from transportation and infrastructure to environmental systems and public policy.
Arura Analytics exists to bring research-grade rigor to any problem complex enough to need it. Whether the question involves transportation systems, water and infrastructure, public policy, environmental risk, or a domain we haven’t taken on yet, our approach stays constant: rigorous modeling, honest validation, and findings translated into decisions people can act on. Our vision is to be the analytical partner organizations turn to when a problem is too important for guesswork — expanding our disciplines as the questions demand, without ever loosening our standards.
Rigorous analysis, built for decisions
Every engagement pairs peer-reviewed methodology with a deliverable your team can actually act on — a validated model, a technical report, or a dashboard your staff can run themselves.
Predictive Modeling & Machine Learning
Building and validating models — ensemble methods, deep learning, time-series and longitudinal models — for prediction, classification, and condition-assessment problems.
- Trained & validated models
- Performance benchmarking (R², RMSE, cross-validation)
- Technical documentation
Infrastructure & Asset-Management Analytics
Deterioration modeling, condition rating, and structural health monitoring for transportation and infrastructure assets, informing proactive maintenance and capital planning.
- Deterioration & condition-prediction models
- Asset prioritization frameworks
- Agency-ready technical reports
Public Policy & Equity Analysis
Composite index construction and quantitative evaluation to measure how policies and infrastructure investments affect different populations.
- Composite equity indices
- Scenario-based policy evaluation
- Disparity & impact analysis reports
Optimization & Decision Support
Network flow, scheduling, and resource-allocation models for operations under real-world time and capacity constraints, delivered through interactive dashboards.
- Optimization & scheduling models
- Interactive dashboards (Streamlit, Tableau)
- Scenario-planning tools
Agentic AI & LLM-Driven Pipelines
Design of autonomous, multi-step analytical workflows that reduce the manual burden of applying machine learning pipelines to new datasets and research questions.
- Agentic AI architecture design
- Pipeline prototyping
- Integration guidance
Grant & Proposal Support
Survey design, data collection planning, and technical writing support for agencies and research teams pursuing federally or state-funded analytical projects.
- Proposal narrative & methodology sections
- Technical reports
- Peer-review-ready manuscripts
State DOTs, transportation authorities, and public agencies get research-grade modeling backed by a track record of DOT, AASHTO, and MDOT-funded work.
- Transportation revenue & demand forecasting
- Infrastructure condition assessment
- Equity & disparities analysis
- Grant-funded research partnership
Organizations needing a custom predictive model, a data pipeline, or a second technical opinion get direct access to a research scientist, not an account manager.
- Custom ML model development
- Data pipeline & agentic AI systems
- Decision-support dashboards
- Technical advisory & review
Discovery
We scope the problem, the data you have available, and the decision the analysis needs to support.
Modeling & Analysis
We build, validate, and stress-test models against your data and real-world constraints.
Delivery & Handoff
You get technical documentation, dashboards, or reports your team can act on and maintain going forward.
Guidance across disciplines
The Advisory Board of Arura Analytics brings together expertise across data science, applied mathematics, infrastructure systems, and public policy. Board members provide strategic guidance on research direction, methodology, and real-world application of analytical insights.
Dr. Zeinab (Shooka) Bandpey
Ph.D., Industrial & Computational Mathematics · Research ScientistDr. Zeinab (Shooka) Bandpey is a Research Scientist with a Ph.D. in Industrial and Computational Mathematics from Morgan State University, specializing in advanced machine learning and computational modeling for complex, real-world systems. Her dissertation developed just-in-time scheduling and network flow optimization models for urban snow-emergency operations. Her work today focuses on applying data-driven methods to challenges in transportation, infrastructure, engineering, and public-sector decision-making.
As a Postdoctoral Research Associate in Civil Engineering at Morgan State University, she built longitudinal progression models spanning 26 years of observational records across more than 5,000 monitored systems, applied time-series clustering and PCA to uncover hidden deterioration regimes in Maryland’s bridge inventory, and constructed composite equity indices linking socioeconomic, behavioral, and spatial factors to transportation outcomes. She has served as Co-Principal Investigator on more than $350,000 in research funded by the U.S. Department of Transportation, AASHTO, and the Maryland Department of Transportation, and has an invention disclosure submitted for an agentic AI framework built for autonomous, multi-step scientific decision support.
She has an extensive publication record with contributions in bridge health monitoring, urban infrastructure analysis, transportation equity, and public-safety analytics, and her research has been presented at conferences including the ASCE International Conference on Transportation & Development, ASCE Structures Congress, and the International Bridge Conference. She also serves as a Scientific Editor for the ASCE Journal of Computing in Civil Engineering and as an Associate Editor of the Bulletin of the Kerala Mathematics Association.
Dr. Bandpey’s interdisciplinary approach and strong analytical foundation enable effective collaboration across technical and non-technical stakeholders, translating complex data into actionable insights for engineers, agency scientists, and policymakers alike.
Areas of Expertise
Dr. Nasim Pica, P.E.
Ph.D., Environmental Engineering · Professional Engineer · Senior Environmental EngineerDr. Nasim Pica is a Professional Environmental Engineer with more than 15 years of experience spanning water and wastewater treatment, water reuse, treatment process optimization, emerging contaminants, and environmental remediation. Her career bridges academic research and engineering practice, with a particular focus on developing, evaluating, and optimizing treatment systems for complex municipal and industrial water challenges.
Dr. Pica’s research has focused extensively on water treatment and resource recovery, including treatment and beneficial reuse of industrial and produced waters, optimization of physical, chemical, biological, and electrochemical treatment processes, and treatment of persistent contaminants. Her research has included optimizing electrocoagulation for metals removal from produced water, evaluating treatment strategies to enable produced-water reuse, investigating water-quality impacts on industrial processes, and developing treatment approaches for PFAS, 1,4-dioxane, and chlorinated contaminants.
Her research has resulted in peer-reviewed publications in leading environmental and engineering journals and has included federally funded research focused on emerging contaminant characterization, treatment, and remediation. Her work has explored technologies ranging from biological and bioelectrochemical treatment to adsorption, membrane separation, advanced oxidation, and electrochemical destruction, with an emphasis on understanding treatment mechanisms and optimizing process performance.
In professional practice, Dr. Pica has led multidisciplinary water and environmental projects involving drinking water, industrial wastewater, water reuse, groundwater treatment, and optimization of existing water and wastewater treatment systems. Her experience spans treatment-process evaluation and troubleshooting, feasibility studies, pilot testing, process design and optimization, technology selection, commissioning and startup, and development of integrated treatment trains. She has worked with conventional and advanced treatment processes including biological treatment, clarification and solids separation, filtration, adsorption, ion exchange, membrane systems, and advanced contaminant treatment technologies.
Dr. Pica also brings extensive experience evaluating water and wastewater infrastructure from a broader operational and sustainability perspective. Her work includes assessing water supply and water quality, identifying opportunities for water reuse and reduced freshwater demand, evaluating treatment-system capacity and performance, and developing technically and economically practical strategies for industrial facilities, utilities, and large infrastructure developments.
She has held technical and leadership roles with WSP, Antea Group, Weston Solutions, and CDM Smith, where she has led multidisciplinary teams and complex programs for federal, municipal, and industrial clients. Her combination of research and engineering practice allows her to approach water challenges from both a scientific and systems perspective—from contaminant chemistry and treatment mechanisms to full-scale treatment performance, process optimization, water reuse, and long-term water-management strategy.
Areas of Expertise
Dr. Mehdi Shokouhian
Ph.D., Civil Engineering · Tenured Associate Professor, Morgan State UniversityDr. Mehdi Shokouhian is a Tenured Associate Professor of Civil Engineering at Morgan State University, specializing in high-performance structural materials, sustainable infrastructure, and data-driven health monitoring.
He earned his Ph.D. in Civil Engineering from Tsinghua University in Beijing, China (2015), where his research focused on section resistance and ductility in hybrid flexural members using high-strength steel. His proposed ductility-based classification system was adopted into the Chinese Design Standard (Specification of High Strength Steel Design).
Following his doctoral studies, Dr. Shokouhian joined Morgan State University in 2015 as a Postdoctoral Research Associate. He subsequently advanced through the academic ranks as a lecturer and tenure-track Assistant Professor before earning tenure as an Associate Professor in 2023. His teaching repertoire covers undergraduate and graduate courses including Design of Steel Structures, Design of Reinforced Concrete Structures, Bridge Engineering, Earthquake Engineering, and Innovation in Structural Steel Design. Beyond classroom instruction, he mentors doctoral, master’s, and undergraduate researchers, while serving as faculty advisor for student chapters of the American Concrete Institute (ACI) and Chi Epsilon.
As a Principal Investigator and Co-PI, Dr. Shokouhian has secured competitive research funding from major agencies, including the U.S. Department of Transportation, National Science Foundation, U.S. Department of Housing and Urban Development, and Maryland Department of Transportation. His multidisciplinary research program encompasses bioengineered and sustainable concrete, machine learning applications for structural resilience, and structural dynamics. Specifically, he investigates bacteria-based self-healing concrete utilizing microbial-induced calcium carbonate precipitation and integrates recycled steel fibers from waste tires to improve structural durability. He also develops predictive deterioration models for bridge health monitoring, evaluates structural safety lessons from major events like the Francis Scott Key Bridge collapse, models urban mobility and safety using AI, and examines high-strength steel members, fiber-reinforced polymer connections, and offshore wind turbine support structures.
Dr. Shokouhian has published extensively in peer-reviewed journals, alongside conference proceedings, technical reports, and pending patent disclosures through Morgan State’s Office of Technology Transfer. He frequently serves as a media expert on bridge safety for outlets such as WYPR, The Baltimore Sun, and The Baltimore Banner. In recognition of his scholarly leadership, Dr. Shokouhian was appointed a Fulbright Specialist by the U.S. Department of State in 2026. His honors also include the American Institute of Steel Construction Advancing Structural Steel Education Award in 2023 and editorial board appointments for multiple international journals in his field.
Areas of Expertise
A portfolio of applied research
This portfolio highlights a selection of research projects focused on applied machine learning, computational modeling, and data-driven analysis across infrastructure, transportation systems, and public policy. The work reflects an interdisciplinary approach that combines rigorous mathematical methods with real-world datasets to support evidence-based decision-making.
Parametric Analysis of Telecommuting Effects on Transportation Tax Revenues
Context
This research examined how shifts toward telecommuting, online activity, and changes in vehicle technology affect transportation-related tax revenues in metropolitan regions. The study focused on understanding long-term implications for transportation funding mechanisms in the context of evolving travel behavior.
Role
Contributing researcher involved in data analysis, model development, and interpretation of results.
Methods
The study integrated survey data, regional travel datasets, and mobility trend data to develop predictive models of telecommuting behavior and transportation revenue impacts. Methods included statistical analysis, discrete choice modeling, and supervised machine learning techniques such as logistic regression, decision trees, random forests, and support vector machines. Parametric analyses were conducted to evaluate the sensitivity of revenue streams to changes in travel demand and vehicle usage patterns.
Outcomes
The research provided quantitative insights into how increased telecommuting and shifts in vehicle miles traveled influence transportation tax revenues. Findings supported scenario-based evaluation of alternative funding strategies and informed discussions on adaptive policy mechanisms for sustainable transportation finance.
Methods and findings based on publicly funded academic research; full technical documentation available upon request.
Uncovering Deterioration Patterns in Maryland’s Steel Bridges
Context
Maryland’s steel bridge inventory ages under widely varying environmental and traffic loads, and aggregate condition ratings can mask meaningfully different decline trajectories between structures. This research examined multi-decade inspection records to surface those hidden patterns and support proactive, rather than reactive, maintenance planning.
Role
Co-author; led the time-series clustering and principal component analysis of individual bridge deterioration trajectories.
Methods
Applied time-series clustering and PCA to 1,378 individual bridge trajectories drawn from 26 years of National Bridge Inventory records, distinguishing symmetric from asymmetric deterioration regimes not visible in aggregate statistics.
Outcomes
Identified distinct deterioration subgroups within the bridge inventory to inform asset-management prioritization. Published in Symmetry (2025); related machine learning deterioration-prediction work from the same research program was presented at ASCE Structures Congress (2023, 2026) and the International Bridge Conference (2024).
Methods and findings based on publicly funded academic research conducted with Maryland Department of Transportation bridge inspection data; full technical documentation available upon request.
Investigating Transportation Equity in Maryland: An AI-Based Approach
Context
This research examined how commute burdens and access to transportation resources are distributed across socioeconomic and demographic groups in Maryland, in support of state transportation planning and funding decisions.
Role
Co-developer of the equity modeling framework, contributing to composite index construction and regression analysis; co-authored the related AASHTO-funded proposal.
Methods
Constructed composite equity indices integrating socioeconomic, behavioral, and spatial covariates, and applied regression-based and machine-learning models to explain differential transportation outcomes across populations.
Outcomes
Findings informed the AASHTO-funded Investigation of Commute Equity Using Machine Learning Techniques project and were presented at the ASCE International Conference on Transportation & Development in Atlanta, GA.
Methods and findings based on publicly funded academic research; full technical documentation available upon request.
Integrating Machine Learning for Enhanced Safety and Crime Analysis in Maryland
Context
This research investigated patterns in crime and public-safety data across Maryland to identify contributing risk factors and support data-driven safety planning at the state and local level.
Role
Contributing researcher; supported model development and validation alongside the study’s co-authors.
Methods
Integrated ensemble machine learning techniques with spatial and socioeconomic datasets to model safety outcomes and identify contributing factors across regions.
Outcomes
Published in Applied Sciences (2025); the work extended the same disparities-research framework used in Dr. Bandpey’s transportation equity research to public-safety outcomes.
Methods and findings based on publicly funded academic research; full technical documentation available upon request.
Analyzing the Impact of Flexible Work Policies on Urban Traffic Patterns
Context
As remote and hybrid work reshaped commuting behavior, this research examined how flexible-work policies have changed urban traffic patterns, funded through the U.S. DOT-supported Center for Multi-Modal Mobility in Urban, Rural, and Tribal Areas (CMMM).
Role
Co-Principal Investigator, responsible for data analysis and development of predictive machine learning models.
Methods
Applied regression-based and quasi-experimental impact analysis to isolate the effect of flexible-work policy shifts on traffic patterns, drawing on regional travel and mobility trend datasets.
Outcomes
Findings support transportation agencies adapting infrastructure investment and demand forecasts to post-pandemic commuting patterns. Two-year award of $159,942, March 2024–March 2026.
Methods and findings based on publicly funded academic research; full technical documentation available upon request.
Just-In-Time Scheduling and Network Flow Models for Urban Snow-Emergency Operations
Context
Cities face hard constraints on time, equipment, and crew capacity when clearing roads during winter storm emergencies. This doctoral dissertation research addressed how to allocate limited snow-removal resources efficiently across an urban road network.
Role
Sole researcher; Ph.D. dissertation, Morgan State University.
Methods
Developed just-in-time scheduling and network flow optimization models to route vehicles and allocate resources across the road network under time and capacity constraints.
Outcomes
Produced an optimization framework applicable to urban snow-emergency response planning, published in the European Journal of Pure and Applied Mathematics (2021).
Methods and findings based on doctoral academic research; full technical documentation available upon request.
An Agentic AI Framework for Scalable, Data-Driven Decision Support
Context
Applying machine learning pipelines to new datasets and research questions typically requires substantial manual effort at each step. This work developed an AI framework to reduce that burden for autonomous, multi-step scientific analysis.
Role
Co-Principal Investigator; invention disclosure submitted to Morgan State University (IPD 307/2026).
Methods
Designed an agentic architecture using LLM-driven analytical pipelines capable of orchestrating multi-step data analysis tasks with reduced human intervention, applicable across scientific domains.
Outcomes
Formal invention disclosure submitted in 2026. The framework is designed for extension into additional research domains, including biomedical and clinical applications.
Methods and findings based on ongoing academic research; full technical documentation available upon request.
Use of Alternative Water Sources for Salt Brine
Context
Winter road maintenance depends on salt brine production, which conventionally draws on potable water. This research, funded by the Maryland State Highway Administration, investigated whether alternative, non-potable water sources could be used for brine production without compromising performance or cost-effectiveness.
Role
Co-Principal Investigator, with Dr. Ahlam Tannouri.
Methods
Developed a machine learning-based cost-benefit decision-making framework to evaluate the feasibility of alternative water sources for salt brine production, weighing water availability, cost, and operational constraints across candidate sourcing scenarios.
Outcomes
Delivered a decision-making framework to MDOT SHA supporting more sustainable water sourcing for winter road-maintenance operations. $92,299 award, 2021–2022.
Methods and findings based on publicly funded academic research; full technical documentation available upon request.
Machine Learning-Based Bridge Condition Rating from Element-Level Inspections
Context
Bridge owners rely on periodic element-level inspections to assess structural condition, but converting raw inspection data into reliable, forward-looking condition ratings is a persistent challenge for asset managers. This multi-year research program built machine learning models to predict condition ratings and deterioration for concrete and steel bridges across Maryland.
Role
Contributing researcher and co-author across the research program; associated invention disclosure (IPD 245/2025) submitted for a method to evaluate and predict bridge deterioration using machine learning.
Methods
Built and benchmarked machine learning models, including ensemble and tree-based methods, trained on element-level inspection records to predict condition ratings and future deterioration for concrete and steel bridges.
Outcomes
Findings presented at ASCE Structures Congress (2023, 2026) and the International Bridge Conference (2024), and support a submitted invention disclosure for a scalable bridge deterioration prediction method.
Methods and findings based on publicly funded academic research; full technical documentation available upon request.
The Number of Graph Homomorphisms Between Paths and Cycles with Loops
Context
This theoretical research examined the combinatorial structure of graph homomorphisms, structure-preserving mappings, between paths and cycles that include loops, contributing to the foundational mathematics underlying network- and graph-based modeling.
Role
Lead author.
Methods
Derived closed-form counts and structural characterizations of homomorphisms between path and cycle graphs with loops, using combinatorial and algebraic techniques.
Outcomes
Published in Transactions on Combinatorics (2023). The underlying graph-theoretic methods inform the network-flow and graph-based models used in Dr. Bandpey’s applied transportation and infrastructure research.
Peer-reviewed theoretical mathematics research; full technical documentation available upon request.
Long-Term Performance of Adsorptive Media for PFAS Removal
Context
Groundwater and drinking-water supplies affected by PFAS contamination require treatment solutions capable of sustained performance over time. This DoD SERDP-funded program evaluated how well adsorptive media technologies hold up under real-world conditions for removing PFAS from contaminated water supplies.
Role
Principal Investigator on the $1.2M award; directed the federally funded R&D program and its academic partnerships.
Methods
Directed pilot-scale testing and evaluation of adsorptive media performance for PFAS removal, engaging academic collaborators at the University of Minnesota and Texas A&M to bring specialized analytical expertise into the program. Investigated contaminant fate and transport alongside treatment-performance data.
Outcomes
Findings informed technical recommendations and implementation strategies for PFAS treatment at contaminated sites, and were co-authored into peer-reviewed publications, technical guidance, and national conference presentations. $1.2M DoD SERDP award, CDM Smith, 2019–2021.
Methods and findings based on federally funded research; full technical documentation available upon request.
PFAS Regulatory Intelligence Hub
Context
PFAS regulations are evolving rapidly and inconsistently across jurisdictions in the United States and internationally, making it difficult for agencies and industry to track applicable requirements. This DoD ESTCP-funded initiative developed a centralized, interactive tool to track and communicate the shifting regulatory landscape.
Role
Co-Principal Investigator on the $700K award.
Methods
Assembled a multidisciplinary team of engineers, chemists, and physicists to design an interactive hub consolidating global PFAS regulatory requirements, paired with technical strategy development for federal water and environmental programs affected by these regulations.
Outcomes
Delivered a regulatory-intelligence tool used to centralize and communicate evolving PFAS requirements, and supported PFAS treatment-technology evaluations at multiple U.S. military installations. $700K DoD ESTCP award, Weston Solutions, 2021–2024.
Methods and findings based on federally funded research; full technical documentation available upon request.
Identification of Novel PFAS Compounds via Ultra-High-Resolution Mass Spectrometry
Context
Many PFAS compounds present in the environment are not yet characterized by standard analytical methods, limiting understanding of their behavior and risk. This DoD SERDP-funded program at Colorado State University’s Center for Contamination Hydrology used advanced mass spectrometry to identify previously unrecognized PFAS compounds.
Role
Co-Principal Investigator on the $500K award; led multidisciplinary research spanning PFAS, GenX, 1,4-dioxane, and chlorinated solvents.
Methods
Applied ultra-high-resolution mass spectrometry, in collaboration with the National High Magnetic Field Laboratory, to identify novel PFAS compounds and characterize environmental fate, transport, and treatment efficacy of emerging contaminants.
Outcomes
Expanded institutional analytical capabilities and contributed to peer-reviewed publications and scientific reports on emerging contaminant behavior, presented at national and international conferences. Findings were published as Young, Pica, et al., “PFAS Analysis with Ultrahigh Resolution 21T FT-ICR MS: Suspect and Nontargeted Screening with Unrivaled Mass Resolving Power and Accuracy,” Environmental Science & Technology, 56(4), 2455–2465 (2022). $500K DoD SERDP award, Colorado State University, 2017–2019.
Methods and findings based on federally funded research; full technical documentation available upon request.
Treatment and Beneficial Reuse of Oil-and-Gas Produced Water
Context
Oil-and-gas produced water represents a large and growing volume of wastewater in resource-extraction regions. This NSF-supported doctoral research examined whether it could be feasibly treated and reused rather than disposed of. Dissertation: “Optimization of Water Management in Hydraulic Fracturing.”
Role
Lead doctoral researcher, Colorado State University; managed cross-disciplinary collaboration among engineers, economists, chemists, and soil scientists.
Methods
Designed and evaluated reverse osmosis, ultrafiltration, granular activated carbon, and electrocoagulation treatment processes for produced water, combining field investigations, laboratory studies, and environmental-impact assessment of reuse scenarios.
Outcomes
Produced six first-author, peer-reviewed papers from the doctoral research — including Pica et al., “Produced water reuse for irrigation of non-food biofuel crops: Effects on switchgrass and rapeseed germination, physiology and biomass yield” (2017) — and presented findings at national conferences and corporate meetings, informing water-management and reuse decisions in the oil-and-gas sector. NSF-supported research, Colorado State University, 2012–2016.
Methods and findings based on doctoral academic research; full technical documentation available upon request.
Machine-Learning-Based PFAS Forensic Application
Context
Determining the source and history of PFAS contamination at a site is often difficult using conventional analytical methods alone. This work developed a machine-learning approach to support PFAS forensic analysis.
Role
Contributing researcher and co-inventor.
Methods
Developed a machine-learning-based application to support PFAS source identification and forensic analysis, contributing to associated intellectual-property development.
Outcomes
Contributed to intellectual-property development supporting PFAS forensic analysis capabilities. CDM Smith, 2019–2021.
Methods and findings based on applied industry research; full technical documentation available upon request.
Water & Environmental Due Diligence for Hyperscale Data-Center Development
Context
Hyperscale data-center development requires substantial water resources for cooling, raising water-supply, water-quality, and long-term operational risk questions that must be resolved during site due diligence.
Role
Senior Environmental Engineer / Project Manager, leading water and environmental evaluation workstreams.
Methods
Evaluates water supply, water quality, wastewater, PFAS, permitting, and treatment needs for property and infrastructure development, translating complex water-resource and infrastructure constraints into decision-relevant technical findings.
Outcomes
Supports due-diligence decisions for critical-infrastructure and data-center clients navigating long-term water-resource and regulatory risk. WSP, 2025–present.
Ongoing professional engagement; full technical documentation available upon request.
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