Renewables, TRIRIGA & New Products: The MAS Modules That Didn't Exist in 7.6

Who this is for: Maximo administrators, IT architects, project managers, and operations leaders evaluating the newest additions to the MAS 9 suite — particularly organizations managing renewable energy assets, large building portfolios, or integrating MAS with enterprise systems like SAP, Oracle, or Workday.

Estimated read time: 10 minutes

The Products That Signal Where IBM Is Going

Every enterprise software platform has features that maintain the past and features that bet on the future. In MAS 9, most of what we have covered in this series falls into the first category — Manage, Health, Monitor, Predict, even Visual Inspection all evolved from capabilities IBM had or was building before the MAS rebranding.

This post covers the other category: the products that did not exist in Maximo 7.6 at all.

Maximo Renewables came from IBM's acquisition of Prescinto, a renewable energy analytics company. TRIRIGA — IBM's standalone Integrated Workplace Management System for two decades — was reimagined and absorbed into MAS as Real Estate & Facilities Management. And the connector ecosystem represents IBM's acknowledgment that MAS cannot exist as an island — it must integrate with SAP, Oracle, Workday, and IBM's own AI and sustainability platforms.

These three areas are where IBM is investing new development dollars. Understanding them tells you not just what MAS can do today, but where the platform is heading over the next five years.

Maximo Renewables: The Prescinto Acquisition

Why Renewables Needed Its Own Module

Traditional asset management — the Manage world of work orders, preventive maintenance, and inventory — works fine for renewable energy assets at a basic level. A wind turbine is an asset. A solar panel is an asset. You can create PMs for them, track inspections, and manage spare parts.

But renewable energy operations have unique characteristics that generic asset management does not address:

  • Performance is weather-dependent. A turbine producing 60% of rated capacity might be performing perfectly in low-wind conditions or terribly in high-wind conditions. Without weather normalization, you cannot tell the difference.
  • Energy loss diagnosis requires specialized models. When a solar plant underperforms, the causes range from soiling and shading to inverter degradation and connection losses. General-purpose analytics cannot distinguish between these without domain-specific training.
  • Portfolio visibility is critical. Independent power producers and utilities manage dozens to hundreds of renewable sites simultaneously. They need a single-pane view of generation performance across the entire fleet — not site-by-site navigation.
  • Inspection at scale requires automation. A solar farm with 100,000 panels cannot be manually inspected for hotspots. Drone-based thermal imaging is the only practical approach, and it requires specialized analysis software.

IBM could have built this from scratch. Instead, they acquired Prescinto — a company that had already solved these problems with pre-trained AI models and purpose-built renewable energy analytics. The result is Maximo Renewables: three modules that bring specialized capability into the MAS ecosystem.

Module 1: Monitor

The Monitor module provides portfolio-level visibility across all renewable energy sites.

Feature — What It Does

Portfolio Map View — Displays all renewable sites on a single geographic map with color-coded status indicators

Asset Health Overview — Shows health status across wind farms, solar plants, and battery storage with color-coded severity

Real-Time Performance — Near real-time comparison of actual generation versus target metrics, normalized for weather

Multi-Site Comparison — Side-by-side performance comparison across 70+ sites simultaneously

The key differentiator is weather normalization. Monitor does not simply show you megawatt output — it shows you output relative to expected output given actual weather conditions. A solar farm generating 80% of nameplate capacity on a partly cloudy day might be performing at 98% of expected output. Without this normalization, operators make wrong decisions constantly — dispatching maintenance crews to sites that are performing fine given the conditions, while missing actual underperformance at other sites.

For a renewable energy operator managing a diversified portfolio — wind farms in the Midwest, solar plants in the Southwest, battery storage facilities on the coast — Monitor is the single screen where the operations center starts every morning.

Module 2: Analyze

The Analyze module is where Prescinto's AI heritage shows most clearly.

Feature — What It Does

Pre-Trained Data Science Models — AI models specifically trained for renewable energy performance patterns — not generic anomaly detection

Energy Loss Waterfall — Cascading diagram showing exactly where energy losses occur in the generation chain

Root Cause Analysis — AI identifies specific causes of underperformance: soiling, shading, degradation, inverter issues, connection losses

Target vs. Actual Generation — Gap analysis that drives corrective work orders when gaps exceed thresholds

Condition-Based Maintenance — Triggers maintenance work orders based on detected performance degradation, not fixed time intervals

The energy loss waterfall deserves special attention. When a solar plant generates less than expected, the loss can occur at multiple points: the panels themselves (soiling, degradation, shading), the string-level wiring (connection losses, mismatch), the inverters (efficiency degradation, partial failure), and the grid connection (curtailment, transformer losses). The waterfall diagram visualizes each loss category as a cascading step from theoretical maximum output to actual output.

This is not something you can build with generic analytics. The pre-trained models understand the physics of renewable energy generation and can decompose total loss into specific, actionable categories. A maintenance planner looks at the waterfall and knows: 40% of our losses are from soiling (schedule panel cleaning), 25% from inverter degradation (dispatch an electrician), and 15% from shading (nothing to do — trees grew, revisit during site design for next plant).

The root cause analysis feeds directly into work order generation in Manage. When Analyze identifies that soiling is costing a site $50,000 per month in lost generation, it can trigger a cleaning work order with the economic justification already calculated.

Module 3: Drone Thermography

The Drone Thermography module addresses the inspection problem that makes solar farm maintenance uniquely challenging.

Feature — What It Does

Thermal Image Analysis — Detects hotspots, damaged cells, and connection issues in solar panels from drone-captured thermal imagery

Systemic Loss Detection — Identifies patterns of losses across entire panel arrays — not just individual panel defects

Automated Reporting — Generates inspection reports from drone imagery with defect classification and severity ratings

Visual Integration — Overlays thermal data on plant layout maps for spatial context

A solar panel hotspot — a cell or connection point operating at significantly higher temperature than surrounding cells — indicates a defect that reduces output and can eventually cause fire or structural damage. In a utility-scale solar farm, there are hundreds of thousands of individual cells. Manual inspection is impossible. Drone-mounted thermal cameras capture the entire plant in hours, but the resulting dataset — thousands of thermal images — requires automated analysis to be useful.

Drone Thermography processes these images, identifies hotspots by severity, classifies the defect type (damaged cell, bypass diode failure, string connector issue, junction box overheating), and generates both a spatial map of defects and an inspection report suitable for maintenance planning.

The systemic loss detection is particularly valuable. If one panel has a hotspot, that is a local defect. If twenty panels in the same string all show elevated temperatures, that is a systemic issue — likely an inverter problem, a wiring fault, or a design deficiency that affects the entire string. Drone Thermography distinguishes between isolated defects and systemic patterns, which changes the maintenance response entirely.

MAS Integration

Renewables does not operate in isolation. Its value multiplies when connected to the broader MAS ecosystem:

Integration Point — What It Enables

Manage — Renewable asset data feeds into Manage for work order generation, spare parts tracking, and maintenance history

Monitor — Performance alerts integrate with MAS Monitor for a unified IoT dashboard spanning both renewable and traditional assets

Health — Renewable asset health scores flow to MAS Health for portfolio-wide asset scoring and investment prioritization

Predict — Predicted failures integrate with MAS Predict for proactive maintenance scheduling before performance degrades

The integration with Manage is the most immediately practical. When Analyze detects that a wind turbine is underperforming due to blade degradation, it creates a work order in Manage with the asset, the failure description, the priority (based on economic impact), and the recommended corrective action. The maintenance planner in Manage sees this work order alongside all other maintenance work and schedules it using the same resource allocation tools they already use.

Who Benefits Most

Organization Type — Primary Use Case

Wind Farm Operators — Turbine performance optimization, downtime reduction, blade condition monitoring

Solar Farm Operators — Panel efficiency monitoring, soiling detection, inverter health, drone-based inspection

Battery Storage Operators — Battery degradation tracking, capacity optimization, thermal management

Independent Power Producers (IPPs) — Multi-technology portfolio management across wind, solar, and storage

Utilities with Renewable Portfolio — Unified lifecycle management of renewable assets alongside traditional generation, transmission, and distribution

If your organization manages renewable energy assets at any meaningful scale — more than a few megawatts of installed capacity — Renewables should be on your evaluation list. The Prescinto AI models are purpose-built for this domain, and the integration with the broader MAS suite means your renewable assets participate in the same health scoring, predictive maintenance, and work management workflows as every other asset class.

TRIRIGA Becomes Maximo Real Estate & Facilities Management

The Backstory

IBM TRIRIGA has been the leading Integrated Workplace Management System (IWMS) for two decades. It managed corporate real estate portfolios, space utilization, lease administration, capital planning, and workplace services for some of the largest enterprises in the world.

But TRIRIGA lived in its own world. It had its own deployment model, its own data model, its own integration patterns, and its own user base. When IBM built MAS, TRIRIGA was conspicuously absent — the facilities management team used TRIRIGA, the maintenance team used Maximo, and the two systems talked to each other through integration middleware when they talked at all.

MAS 9.1 changes this. TRIRIGA has been reimagined as Maximo Real Estate & Facilities Management (MREF) — a native MAS application that runs on the same platform, shares the same deployment model, and integrates directly with Manage, Health, Monitor, and Predict without middleware.

This is not a rebrand. It is a re-architecture. MREF runs on the MAS operator framework, deploys on OpenShift alongside every other MAS application, and shares the same AppPoints licensing model. For organizations that previously ran both Maximo and TRIRIGA as separate platforms with separate teams, the unification is significant.

The Eight Modules

MREF delivers eight functional modules that cover the full spectrum of facility and real estate management:

Space Management

Track office space utilization, maintain digital floor plans, and monitor occupancy at the building, floor, and zone level. Space management provides the data foundation for decisions about consolidation, expansion, and redesign — answering the question every CFO asks after seeing the real estate bill: "Are we actually using all this space?"

Occupancy tracking integrates with IoT sensors (through MAS Monitor) to provide real-time and historical utilization data, not just allocated headcounts. The difference matters: a floor allocated for 200 people but averaging 80 occupants is a consolidation opportunity that traditional space management — based on assigned seats, not actual presence — would miss.

Room and Desk Reservations

Meeting room booking and desk reservation with hot desking support. In the post-pandemic workplace where hybrid work is standard, hot desking has gone from a niche practice to a baseline requirement. Employees arriving on-site need to find and reserve a desk, and meeting organizers need rooms that accommodate their actual attendance — not a conference room for 20 when 5 people are coming.

The reservation system integrates with workplace experience tools and space management to feed utilization data back into planning. If Conference Room B is booked 90% of the time but Conference Room D sits empty, facilities managers see this in the data and can make informed decisions about room allocation, equipment investment, and floor redesign.

Move Management

Plan and execute employee and department relocations with structured workflows, approval chains, and coordination across IT (workstation moves), telecom (phone and network), facilities (furniture and signage), and HR (directory updates). A corporate move involving 500 people across three floors is a logistics operation that fails spectacularly without structured management.

Move Management provides the planning tools — floorplan-based visual planning, resource scheduling, move sequence optimization — and the execution tracking to coordinate every team involved in a relocation.

Capital Planning and Facility Condition Assessment (FCA)

Evaluate the condition of building systems (HVAC, electrical, plumbing, roofing, structural) through structured assessments, then plan and prioritize capital investments based on condition scores, remaining useful life, criticality, and budget constraints.

FCA integrates with MAS Health for building system health scores. When a Health model identifies that the HVAC system in Building 7 is degrading faster than expected, that signal feeds into capital planning as a data point for the replacement decision — not just "the HVAC is old" but "the HVAC is degrading at 1.5 times the expected rate and will likely fail within 18 months."

Portfolio Analysis

Evaluate real estate holdings across the entire portfolio for consolidation opportunities, lease versus own decisions, and strategic alignment. Portfolio analysis provides the executive-level view: which buildings are underutilized, which leases are approaching renewal, where capital investment is needed, and how the portfolio aligns with the organization's strategic direction.

For organizations with hundreds of buildings across multiple geographies, portfolio analysis transforms real estate from a cost center managed by spreadsheet into a strategic asset managed by data.

Lease Management

Track commercial leases including terms, renewal dates, options, escalation clauses, and total cost of occupancy. Lease management is a compliance function as much as a financial one — ASC 842 and IFRS 16 lease accounting standards require organizations to track and report lease obligations with precision that spreadsheets cannot reliably deliver.

MREF's lease management tracks critical dates (expiration, renewal option exercise, termination notice), financial terms (base rent, escalation, operating expense pass-throughs), and document management (lease contracts, amendments, correspondence) in a single system of record.

Sustainability

Building energy management, carbon footprint tracking, and environmental performance monitoring. The sustainability module tracks energy consumption by building, floor, and system — enabling targeted efficiency improvements rather than building-wide averages that mask the actual sources of waste.

Carbon tracking integrates with IBM Envizi for ESG reporting, providing the data pipeline from operational energy consumption in MREF to corporate sustainability reports in Envizi. For organizations with carbon reduction commitments or regulatory reporting requirements, this integration eliminates the manual data collection that currently consumes weeks of effort each reporting cycle.

Workplace Experience

Employee-facing tools for workplace engagement, service requests, and facility feedback. Workplace experience is the interface between the facilities team (who manages buildings) and the building occupants (who use them). Service requests, comfort complaints, amenity feedback, and workplace satisfaction surveys flow through this module — giving facilities managers the occupant perspective alongside the operational data.

MAS Integration

MREF's integration with the broader MAS suite is what distinguishes it from standalone TRIRIGA:

Integration — What It Enables

Manage — Facilities work orders generated from MREF flow into Manage — a water leak detected through a service request becomes a work order with the right asset, location, priority, and craft assignment

Health — Building system health scores for HVAC, electrical, plumbing, elevators, and other systems — feeding capital planning with data-driven condition assessments

Monitor — Building IoT data from temperature, humidity, occupancy, air quality, and energy sensors — providing real-time operational visibility and feeding space utilization analytics

Predict — Predictive models for building system failures — identifying HVAC compressors or elevator motors likely to fail before they do

Envizi — ESG and sustainability reporting — operational energy and carbon data from MREF flows to Envizi for corporate-level environmental reporting

The Manage integration alone justifies the unification for many organizations. Previously, a TRIRIGA service request for a leaking pipe would generate a notification that someone manually entered into Maximo as a work order. Now, the service request in MREF becomes a Manage work order automatically — with the building, floor, room, asset, and problem description already populated.

Who Needs MREF

Organization Type — Why MREF Matters

Large Enterprises — Manage corporate real estate portfolio alongside operational technology assets in a single platform

Government Agencies — Track public buildings, space utilization, regulatory compliance, and capital planning across building portfolios measured in thousands

Healthcare — Hospital space management, equipment and facility management unified — operating rooms are both spaces to manage and asset-rich environments to maintain

Higher Education — Campus space management, classroom scheduling, residence hall maintenance, and capital planning across aging building stock

Retail — Store portfolio management with integrated maintenance — lease tracking, space optimization, and facility maintenance in a single view

If your organization currently runs TRIRIGA alongside Maximo, the migration to MREF within MAS should be on your roadmap. If your organization has been managing facilities with spreadsheets, email, and Maximo work orders alone, MREF represents a step-function improvement in visibility and control.

The Connector Ecosystem: MAS Does Not Live Alone

Why Connectors Matter

No enterprise asset management system operates in isolation. Assets generate costs that flow to ERP systems. Procurement decisions pull from supply chain platforms. Workforce assignments interact with HR systems. Sustainability data feeds ESG reporting tools. AI models require data science platforms for training and deployment.

MAS 9 addresses this through a connector ecosystem that provides pre-built, configurable integration with the enterprise systems that matter most. These are not custom point-to-point integrations — they are supported, maintained connectors with defined data mappings, sync patterns, and error handling.

SAP Connector

Capability — Details

Work Order Cost Sync — Work order costs from MAS Manage flow to SAP controlling (CO) for cost center and project accounting

Purchase Requisition Sync — Purchase requisitions created in Manage sync to SAP Materials Management (MM) for procurement processing

Material Master Sync — Material/inventory items sync bidirectionally between Manage item master and SAP material master

Asset Master Sync — Asset records sync between Manage and SAP Plant Maintenance (PM) or Asset Accounting (AA)

GL Account Sync — Chart of accounts and cost center structures sync from SAP Finance (FI) to Manage

For organizations running SAP as their ERP, this connector eliminates the most common integration pain point: the double-entry problem. Without it, a purchase requisition created in Maximo must be manually re-entered in SAP, or a custom MIF integration must be built and maintained. The connector provides this out of the box with configurable mapping rules.

Oracle Connector

Capability — Details

Financial Integration — Work order costs and purchase requisitions sync to Oracle Financials (Cloud or E-Business Suite)

Procurement Sync — Purchase orders created in Manage flow to Oracle Procurement for processing and approval

Inventory Sync — Item and inventory data synchronization between Manage and Oracle Inventory

HR Data Sync — Person/labor records sync from Oracle HCM for craft and labor availability

The Oracle connector supports both Oracle Cloud (Fusion) and Oracle E-Business Suite (EBS) deployments, though the mapping configurations differ between them. Organizations migrating from EBS to Oracle Cloud can maintain MAS integration throughout the transition by reconfiguring the connector rather than rebuilding from scratch.

Workday Connector

Capability — Details

HR/People Sync — Employee records, organizational assignments, and labor qualifications sync from Workday HCM to Manage person records

Cost Center Sync — Financial organizational structures from Workday Financial Management sync to Manage GL account structures

Position and Role Sync — Job profiles and organizational roles in Workday map to Manage craft, skill, and qualification records

For organizations using Workday as their HR system of record, this connector ensures that the person records in Manage — who is available, what crafts they hold, what qualifications they have, which organization they belong to — stay synchronized without manual maintenance. When a new maintenance technician is hired in Workday, their record appears in Manage with the correct craft, qualification, and organizational assignment.

TRIRIGA Connector

Capability — Details

Space-to-Location Sync — TRIRIGA space definitions sync to Manage locations for facility-aware work management

Service Request to Work Order — TRIRIGA service requests generate Manage work orders for maintenance execution

Asset Sharing — Building system assets shared between TRIRIGA facility management and Manage maintenance

Occupancy Data — Space occupancy data from TRIRIGA informs maintenance scheduling (avoid occupied areas)

Note: This connector exists for organizations still running standalone TRIRIGA alongside MAS Manage. For organizations migrating to MREF within MAS 9.1, the TRIRIGA connector is replaced by native MREF integration — no connector required because the applications share the same platform.

Envizi Connector

Capability — Details

Energy Consumption Sync — Building and equipment energy consumption data from MAS flows to Envizi for aggregation and reporting

Carbon Emissions Data — Calculated carbon emissions from energy consumption sync to Envizi for scope 1, 2, and 3 reporting

Utility Bill Integration — Utility billing data captured in Manage (or MREF) flows to Envizi for cost and consumption tracking

ESG Report Data — Operational sustainability metrics feed Envizi's ESG reporting framework for investor and regulatory disclosures

The Envizi connector is particularly relevant for organizations with sustainability commitments. Envizi is IBM's ESG reporting platform, and the connector provides the data pipeline from operational reality (how much energy this building consumed, how many gallons of fuel that fleet burned) to corporate reporting (our scope 1 emissions decreased 12% year over year).

watsonx.ai Connector

Capability — Details

Custom AI Model Integration — Deploy custom-trained AI models from watsonx.ai into MAS workflows — custom failure prediction, anomaly detection, or natural language processing models

Foundation Model Access — Leverage IBM's foundation models (Granite series) for natural language work order analysis, knowledge extraction, and technician assistance

Model Lifecycle Management — Manage AI model versions, training data, and deployment lifecycle from watsonx.ai with inference in MAS

Trend Analysis (MAS 9.1) — watsonx.ai-powered trend analysis for asset performance, maintenance patterns, and operational KPIs

The watsonx.ai connector is about extensibility. MAS includes pre-built AI capabilities in Health (scoring), Predict (failure prediction), Visual Inspection (image analysis), and Assist (knowledge retrieval). But organizations with data science teams may want custom models — a failure prediction model trained specifically on their equipment, an anomaly detection model tuned to their operational patterns, or a natural language model that understands their maintenance terminology. The watsonx.ai connector enables this: train in watsonx.ai, deploy in MAS.

Cloud Pak for Data Connector

Capability — Details

Data Virtualization — Access MAS data alongside data from other enterprise systems through Cloud Pak for Data's virtualization layer — no data movement required

Advanced Analytics — Use Cloud Pak for Data's analytics tools (Jupyter notebooks, SPSS, DataStage) to analyze MAS operational data

Data Governance — Apply Cloud Pak for Data governance policies (data quality, lineage, cataloging) to MAS data

Custom Dashboard Creation — Build custom operational dashboards combining MAS data with data from other enterprise sources

Cloud Pak for Data is IBM's data and AI platform. The connector enables organizations to treat MAS data as a first-class data source within their enterprise data architecture — without extracting it into a separate data warehouse. For organizations that need cross-functional analytics (correlating maintenance costs with production output with financial performance), the data virtualization capability is particularly valuable.

Licensing and Cost Considerations

All three areas covered in this post require additional investment beyond base Manage:

Product — Licensing Model — Notes

Renewables — Separate pricing — Contact IBM for pricing — no standard AppPoints allocation published

Real Estate & Facilities (MREF) — Separate pricing — Contact IBM for pricing — new in MAS 9.1, pricing model still evolving

Connectors — Varies by connector — Some included with MAS, some require additional licensing or the connected product's license

The pricing uncertainty for Renewables and MREF is real. These are new products, and IBM has not published standard AppPoints consumption rates for them in the same way they have for Health, Monitor, or Predict. Your IBM account team or business partner will provide custom pricing based on your portfolio size, user count, and deployment model.

For connectors, the cost is typically modest compared to the applications themselves — but the connected system's licensing is a separate matter. The SAP connector does not include an SAP license. The Envizi connector does not include an Envizi subscription. Budget accordingly.

Implementation Sequencing

If you are evaluating these products, the recommended sequencing is:

For Renewables:

  1. Assess your renewable energy asset portfolio — how many sites, what technologies (wind, solar, battery), what SCADA/IoT data is available
  2. Request an IBM Maximo Renewables demo with your data characteristics
  3. Evaluate data availability: SCADA historian connectivity, weather data feeds, generation metering
  4. Identify a pilot site — ideally one with known performance issues where Analyze can demonstrate value
  5. Deploy Monitor for portfolio visibility, then Analyze for AI-driven loss detection, then Drone Thermography for solar-specific inspection

For MREF:

  1. Assess your current facilities management tooling — what are you using today, what are its limitations
  2. Evaluate the MREF business case — does your organization have enough buildings, enough complexity, enough integration need to justify a dedicated IWMS
  3. Map current space management, lease administration, and capital planning processes to MREF modules
  4. Identify a pilot building for initial deployment
  5. Deploy in phases: space management and reservations first (quick wins), then lease management and capital planning (higher complexity)

For Connectors:

  1. Inventory your current MAS integration points — what systems does Maximo currently integrate with, and how
  2. Identify which pre-built connectors replace existing custom integrations — this is often the fastest ROI
  3. Deploy connectors in priority order: ERP connector first (SAP or Oracle), then HR connector (Workday), then specialty connectors (Envizi, watsonx.ai)

Key Takeaways

  • Maximo Renewables is purpose-built, not generic — the Prescinto acquisition brought pre-trained AI models for wind, solar, and battery storage that generic analytics cannot replicate
  • Three Renewables modules serve different needs — Monitor for portfolio visibility, Analyze for AI-driven root cause and loss detection, Drone Thermography for solar panel inspection at scale
  • TRIRIGA's absorption into MAS is a re-architecture, not a rebrand — MREF in MAS 9.1 runs natively on the MAS platform with direct integration to Manage, Health, Monitor, Predict, and Envizi
  • MREF's eight modules cover the full IWMS spectrum — space, reservations, moves, capital planning, portfolio analysis, leases, sustainability, and workplace experience
  • The connector ecosystem eliminates custom integration — pre-built connectors for SAP, Oracle, Workday, TRIRIGA, Envizi, watsonx.ai, and Cloud Pak for Data replace hand-coded MIF integrations
  • None of these products existed in Maximo 7.6 — they represent net-new capability, not upgrades of existing features

References

Series Navigation:

Previous: Part 18 — Paid Add-Ons: HSE, Spatial, Service Provider, ACM & Maximo IT
Next: Part 20 — Industry Solutions: Aviation, Transportation & Utilities

View the full MAS FEATURES series index

Part 19 of the "MAS FEATURES" series | Published by TheMaximoGuys

Renewables, TRIRIGA, and the connector ecosystem represent IBM's clearest signal about where MAS is heading: domain-specific intelligence, unified facility and asset management, and open integration with the enterprise systems that surround it. If your organization touches renewable energy, manages buildings, or integrates with SAP and Oracle — these are not optional evaluations. They are strategic decisions that shape how your MAS deployment fits into the broader enterprise architecture.