Ehsan Ullah: 2025

2025 Airline Initiatives

Airlines worldwide are launching a variety of career initiatives in 2025 and 2026 to address workforce shortages, enhance diversity, and prepare for future growth. Here’s an overview of notable programs and strategies:

✈️ Pilot Training & Career Pathways

Delta Air Lines – Propel Career Path Program

Delta’s Propel program offers a direct path to a pilot career, providing mentorship from Delta pilots, a qualified job offer with Delta Air Lines and its subsidiary Endeavor Air, and a seamless transition through a single interview.

United Airlines – Aviate Academy

United’s Aviate Academy, located in Goodyear, Arizona, is a flight training school owned and operated by United Airlines. It serves as the primary training facility for United Aviate, United’s pilot career development program, aiming to train 5,000 pilots by 2030, with at least 50% women or people of color.

Aer Lingus – Future Pilot Programme

Aer Lingus has reopened its Future Pilot Programme, offering aspiring aviators the chance to train and fly for the airline. The 14-month training course is fully sponsored, aiming to develop 90 pilots over five years. Applications are open until February 10, 2025. The airline is encouraging more female applicants to improve diversity.

👩‍💼 Graduate & Internship Programs

Delta Air Lines – Student & Early Careers

Delta offers internship, MBA, co-op, and rotational opportunities for students and recent graduates. These programs provide exposure to various departments and include benefits like travel privileges and professional development.

Southwest Airlines – Campus Reach Program

Southwest’s Campus Reach program identifies and engages future employees at an early age, offering internships and full-time opportunities across various departments. Interns receive compensation, travel benefits, and housing stipends.

Jetstar – 2026 Graduate Program

Jetstar is recruiting new graduates for its 2026 Graduate Program, focusing on developing future leaders within the airline. Applications have closed, but the program reflects Jetstar’s commitment to nurturing talent.

🧰 Technical & Maintenance Training

American Airlines – Aviation Maintenance Partnership

American Airlines has partnered with George T. Baker Aviation Technical College in Miami to support students pursuing careers in aviation maintenance, addressing the growing demand for skilled technicians.

Cathay Pacific – Engineering Graduate Trainee Programme

Cathay Pacific offers a 36-month Engineering Graduate Trainee Programme with four different pathways, providing unique experiences and exposure to becoming a professional engineer within the airline.

🌍 Diversity & Inclusion Initiatives

United Airlines – Commitment to Diversity

United Airlines’ Aviate Academy aims to train 5,000 pilots by 2030, with at least 50% women or people of color, addressing the need for greater diversity in the cockpit.

EasyJet – Recruitment Drive for Armed Forces Veterans

EasyJet has initiated a recruitment drive aimed at employing veterans, encouraging them to utilize their highly transferable skills in various roles, including engineering and cabin crew. This initiative is part of EasyJet’s effort to hire more older workers.

📈 Industry-Wide Hiring Trends

Lufthansa Group – Hiring Plans for 2025

Lufthansa Group aims to hire approximately 10,000 new employees in 2025, focusing on flight attendants, ground staff, technical experts, administrative staff, and pilots. The recruitment emphasis is on subsidiaries like Lufthansa Technik, Austrian Airlines, and Eurowings.

Pilot Hiring Outlook

The airline industry is navigating a period of transformation marked by fluctuating hiring patterns, production delays, and a persistent pilot shortage. After a significant slowdown in pilot recruitment in 2024, industry experts project a return to more normalized hiring levels in 2025.

If you’re interested in specific programs or opportunities, feel free to ask for more details!

From Blogger iPhone client

The global adoption of technological innovation and robotics in 2025 and 2026 is accelerating across industries, driven by advancements in artificial intelligence (AI), automation, and the need for sustainable solutions. Here’s an overview of key trends and developments:

🤖 Key Robotics Trends in 2025–2026

AI-Driven Robotics

Robots are increasingly integrating AI capabilities, enabling them to perform complex tasks with greater autonomy. This includes analytical AI for data processing, physical AI for real-world interactions, and generative AI for adaptive learning. Such advancements are enhancing robot efficiency in manufacturing, healthcare, and service industries.

Humanoid Robots

Humanoid robots are being developed to operate in environments designed for humans, such as warehouses and factories. Companies like Tesla, Agility Robotics, and Figure are leading in this space, aiming to address labor shortages and improve operational efficiency.

Sustainable Robotics

Robotics is contributing to sustainability goals by improving energy efficiency and reducing waste. For instance, robots are being used in precision agriculture to minimize chemical use and in manufacturing to optimize resource utilization.

Collaborative Robots (Cobots)

Cobots are designed to work alongside humans, enhancing productivity and safety. Their adaptability makes them suitable for small and medium-sized enterprises, facilitating automation without extensive infrastructure changes.

Digital Twins

The use of digital twins—virtual replicas of physical systems—is enabling real-time monitoring and optimization of robotic operations. This technology is particularly beneficial in manufacturing and logistics for predictive maintenance and process improvements.

🌍 Global Market Outlook

Market Growth: The global robotics market is projected to grow from $71.78 billion in 2025 to $150.84 billion by 2030, reflecting a compound annual growth rate (CAGR) of 16.0%.
Intelligent Robotics: The intelligent robotics segment is expected to expand from $13.99 billion in 2025 to $50.33 billion by 2030, with a CAGR of 29.2%.
Service Robots: The service robot market, including applications in healthcare and hospitality, is anticipated to reach $24.64 billion by 2026.

🏭 Industry Applications

Manufacturing: AI and robotics are being utilized for predictive maintenance, reducing unplanned downtime and saving costs. Companies like Coca-Cola and Siemens Energy are adopting these technologies to enhance operational efficiency.
Healthcare: Robotic systems are increasingly used in surgeries and patient care, improving precision and reducing recovery times. The medical robotics market is projected to reach $20.6 billion by 2026.
Agriculture: Robots are aiding in tasks like planting, harvesting, and monitoring crop health, contributing to sustainable farming practices and increased yields.

🚀 Future Outlook

As technological advancements continue, the integration of robotics across various sectors is expected to deepen, leading to increased efficiency, sustainability, and innovation. However, challenges such as high initial costs, workforce adaptation, and ethical considerations will need to be addressed to ensure equitable and effective adoption.

Sky runner payload system robot

https://youtube.com/shorts/c7Vo3ZEXfYU?si=M7bvyZJnYSsas1jB

From Blogger iPhone client

https://youtube.com/shorts/ttS0GwHszWU?si=d4smtiYhv-dOnUe7

Bradford KPI for HR

The Bradford Factor is a widely used HR tool for measuring employee absenteeism, particularly short-term and frequent absences. Its reliability and effectiveness depend on how it’s used and the context of the organization. Here’s a breakdown of its reliability:

What the Bradford Factor Measures

It calculates a score based on the formula:

B = S² × D

S = number of absence instances (spells)
D = total number of days absent

This emphasizes frequent short absences more than occasional long ones.

Reliability: Pros

Objective and Simple: Provides a clear, numeric view of absence patterns.
Highlights Frequent Absences: Helps identify patterns that might indicate issues like disengagement, burnout, or abuse.
Consistency: If applied uniformly, it can support fair policy enforcement.

Reliability: Cons and Limitations

Lacks Context: It doesn’t account for legitimate or medically necessary absences (e.g., chronic illness, disability, mental health).
Can Be Misused: Rigid application can lead to unfair disciplinary actions and deteriorate morale.
Not Predictive: It tracks past absences but doesn’t explain why they occurred or predict future behavior.
One-size-fits-all Risk: Doesn’t differentiate between job roles, health statuses, or personal circumstances.

Best Practice for Use

Supplement, don’t replace manager judgment and HR discretion.
Use in combination with other data (e.g., performance reviews, wellness reports).
Apply with sensitivity, especially for employees with protected medical conditions (to avoid legal risks).

Summary

The Bradford Factor is moderately reliable as an early warning or flagging system for absenteeism patterns but is not sufficient on its own for making disciplinary or performance decisions. Its value comes from how thoughtfully and fairly it’s applied within an organization’s broader HR strategy.

Cleaning robots

https://youtube.com/shorts/7aoWJ_chjxw?si=0pmrcifMnyzi8hY-

From Blogger iPhone client

Generating energy

https://youtube.com/shorts/M54tK3lFcVs?si=hjySUgbFiNuAOZCs

From Blogger iPhone client

Synthetic data modeling

In addition to faker, there are several Python libraries and tools that can generate reliable synthetic data, depending on the type (tabular, time-series, image, text, etc.) and the level of realism or constraints you need. Here’s a breakdown by use case:

1. General Tabular Data

SDV (Synthetic Data Vault)
State-of-the-art for realistic synthetic tabular data.
Uses statistical modeling or deep learning (GANs, CTGAN, TVAE).
Ideal for privacy-preserving data generation.
scikit-learn’s make_classification, make_regression, make_blobs
Useful for ML model prototyping.
Generates labeled synthetic data for classification/regression problems.
[Synthpop (via RPy2 for Python)]
If you want to use R’s synthpop from Python.
Great for statistical privacy-preserving synthetic data.

2. Time-Series Data

Timesynth
For generating synthetic time series data.
Supports autoregressive signals, harmonic signals, and noise.
Sktime
Has utilities for time series generation, often used in research.

3. Image / Vision Data

Albumentations / ImgAug
Not for raw image generation, but great for data augmentation.
StyleGAN2 / DALL·E (for advanced users)
Used to generate high-quality synthetic images.

4. Text / NLP Data

TextAttack
For generating paraphrases, adversarial samples.
Useful for NLP model testing.
Transformers (HuggingFace)
Fine-tuned models can generate domain-specific synthetic text.

5. Domain-Specific Libraries

Pydbgen
Like Faker, but tailored for fake databases.
DataSynthesizer
Generates synthetic data with differential privacy guarantees.
Mimesis
Similar to Faker, supports multiple locales and categories.

Would you like suggestions based on a specific type of data or project goal (e.g., simulating customers, financial records, health data)?

From Blogger iPhone client

Find phone

https://youtube.com/shorts/Y0zxyvZdgCc?si=BgpaMPtNnJm2aOy5

From Blogger iPhone client

Talent and hard work

https://youtube.com/shorts/NCz2XTk-Mlw?si=8BsDdi2RxfndKX_T

From Blogger iPhone client

Airline maintenance demand and capacity

To measure demand and capacity for aircraft maintenance, allocate technical staff efficiently, and minimize overall downtime, you need to gather data across several categories: aircraft operations, maintenance needs, workforce management, and resource availability.

Aircraft Operations Data (Demand Drivers)

These determine when and how often maintenance will be needed.

Flight hours and flight cycles per aircraft
Scheduled route plans (frequency, duration, utilization)
Aircraft type and age
Regulatory maintenance intervals (A-check, C-check, D-check, etc.)
Unscheduled maintenance events (e.g., faults reported by pilots or systems)

Maintenance Task Data

Helps define what needs to be done, and how long it takes.

Task duration estimates (man-hours)
Task precedence constraints (dependencies between tasks)
Required qualifications/certifications per task
Special tools or equipment required
Parts and materials needed
Task deadlines (hard or soft constraints)

Technical Staff Data (Capacity)

Used for staff planning and allocation.

Headcount by shift and location
Staff skill levels and certifications (e.g., A&P, avionics)
Work-hour availability per person (after accounting for leave, training, fatigue limits)
Multi-tasking ability or crew specialization
Labor cost per technician/crew

Maintenance Facility & Resource Data

Defines physical capacity and constraints.

Number of bays/hangar slots by aircraft size/type
Tool and equipment availability
Storage and handling constraints for parts
Availability of ground support staff and systems

Scheduling and Planning Data

Helps in integrating the above into schedules.

Aircraft availability windows (downtime allowed without disrupting operations)
Priority rules (e.g., safety-critical before cosmetic)
Slack time between scheduled checks
Lead times for procurement of parts/tools
Historical delay/cancellation data due to maintenance

Performance and Risk Metrics (for optimization)

Used to evaluate and improve the scheduling model.

Total downtime per aircraft
Turnaround time per maintenance event
Technician utilization rate
On-time performance (OTP) impact
Deferred maintenance events count
Maintenance-induced delays or cancellations

Would you like a visual summary or a data model template in Excel or diagram format for these?

From Blogger iPhone client

Sandbox benefits

Great question. Here’s a breakdown of top use cases for a Data Analyst working with:

BigQuery Sandbox, and
Raw Layer of a Data Lake (e.g., DataLake, GCS, or raw zone in a data lakehouse)

1. BigQuery Sandbox – Use Cases for Data Analysts

The BigQuery Sandbox is a free, no-credit-card-required environment, ideal for prototyping and learning. It has usage limits but supports real SQL capabilities.

Top Use Cases:

Ad-hoc SQL Analysis
Run quick queries against public datasets or connected sources for exploratory analysis.
Data Cleaning and Transformation
Use SQL to apply filters, remove duplicates, standardize formats (e.g., dates, currency).
Data Joins Across Tables
Combine datasets using JOIN to enrich or correlate data.
Custom Metric Calculation
Create derived metrics like conversion rates, retention, churn, etc.
Visualization Prototyping
Connect BigQuery Sandbox to tools like Looker Studio (free) for dashboard mockups.
Query Optimization Practice
Analyze execution plans and learn how to optimize SQL using partitioning, clustering, and caching.
Public Dataset Exploration
Leverage Google’s public datasets (e.g., COVID, Census, StackOverflow) for practice and insights.

2. Raw Layer of Data Lake – Use Cases for Data Analysts

The raw layer stores unprocessed, large-volume data — often in formats like JSON, Parquet, or CSV — usually on cloud storage (like GCS, S3, or Azure Data Lake).

Top Use Cases:

Schema Discovery & Data Profiling
Use tools like bq load, bq show, or data catalog to inspect structure, nulls, ranges, outliers.
Data Ingestion Validation
Check if all expected files landed, and validate record counts, file sizes, or timestamps.
Raw-to-Curated Transformation
Write SQL or Spark queries (depending on infra) to shape raw data into usable curated/clean tables.
Change Detection
Compare raw file drops over time for changes in volume, structure, or anomalies.
Data Lineage & Traceability
Understand source system behavior by inspecting raw logs or events (e.g., API payloads, user events).
Staging for Snapshotting
Pull in the raw data to BigQuery staging tables for snapshotting/archival analytics.

Combined Workflow Example:

Analyst pulls raw JSON logs from GCS into BigQuery temporary table.
Cleans and joins with dimension data in curated layer.
Creates KPIs and dashboards in Looker Studio using the sandbox or scheduled queries.

Would you like a visual diagram or cheat sheet to map these layers and use cases?

From Blogger iPhone client

Using big query schedule

To schedule queries in BigQuery, you use Scheduled Queries in the BigQuery UI, the bq command-line tool, or Terraform/API. Here’s how to do it via the UI (most common approach):

Step-by-Step: Schedule a Query in BigQuery UI

Open BigQuery Console
Go to: https://console.cloud.google.com/bigquery
Write Your Query
Open the SQL editor and write your query (e.g., a snapshot insert query).
Click on “Schedule”
You’ll see a “Schedule” button just above the query editor.
Click it and then select “Create new scheduled query”.
Set Job Details
Name your job.
Choose Project and Location (typically US or EU).
Set Frequency
Choose from predefined intervals: Hourly, Daily, Weekly, Monthly, or Custom using cron expressions.
Example (daily at midnight): 0 0 * * *
Set Start Time
Define when the schedule should start.
Destination (optional)
If your query writes results to a table, you can configure the destination table and write behavior (e.g., append or overwrite).
Notification Settings (optional)
Set up email notifications or connect to Cloud Logging.
Save
Click Save to create the scheduled query.

Optional Tips

Permissions Needed:
You need bigquery.jobs.create permission (usually included in roles like BigQuery Admin or Editor).
Monitor Jobs:
Navigate to BigQuery > Scheduled Queries to view or manage existing schedules.

Would you like a CLI version (bq command) or cron-style examples?

From Blogger iPhone client

Asana sync devops

To synchronize Azure DevOps and Asana, you can use integration platforms or custom APIs. Here are your main options:

1. Use a Third-Party Integration Platform

a. Unito

Features: Two-way sync of work items, comments, assignees, status, due dates.
How it works:
Connect Azure DevOps and Asana.
Set rules for how work items/tasks map (e.g., DevOps bugs → Asana tasks).
Ideal for: Teams needing live sync between platforms.

b. Zapier or Make (Integromat)

Zapier example:
Trigger: New work item in Azure DevOps.
Action: Create task in Asana.
Note: Mostly one-way automation (not full sync), but good for simple workflows.

2. Use Azure Logic Apps

Built-in connectors for both Azure DevOps and Asana.
Can build custom workflows, e.g.:
When a new task is created in Asana → create a work item in Azure DevOps.
When a DevOps item is updated → update Asana task status.

3. Build a Custom Integration with APIs

Use Azure DevOps REST API and Asana API.
Sync based on:
Task/work item creation
Status updates
Comments
Ideal if you need full control and have specific logic/fields to sync.

Recommendation

For most use cases, Unito is the fastest and most flexible option.
For enterprise workflows or data governance concerns, Azure Logic Apps may offer better control and security.

Would you like a visual comparison or a step-by-step guide for any of these tools?

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Bloomberg rest api data cost

Accessing Bloomberg data via their REST API is available through Bloomberg’s Enterprise API (BBG API), but it’s not publicly priced like a typical SaaS service. Pricing depends on your use case, data volume, and licensing agreements. However, here’s a general breakdown:

1. Bloomberg Terminal Subscription (Required for API Access)

Cost: ~$2,000–$2,500 USD/month per terminal
Includes access to Bloomberg Desktop API (Excel and limited programmatic use)
Not sufficient for large-scale or automated use

2. Bloomberg Enterprise Data License (for REST API & scalable access)

Base Cost: Starts around $10,000–$25,000 USD/year or more
Additional costs depend on:
Data types (real-time, delayed, historical)
Number of tickers
Fields requested (e.g., last price, market cap, volume, etc.)
Update frequency (snapshot vs. streaming)
Redistribution rights (if you serve data to clients or apps)

3. Bloomberg B-PIPE (Streaming Data for Enterprises)

For real-time market data with low latency
Cost: Starting from $100,000+/year, typically used by large financial institutions

Alternatives for Developers/Smaller Firms

If you only need limited financial data:

Refinitiv, Xignite, Quandl, or Polygon.io may offer more affordable REST API access.
Some of them have freemium tiers or pay-per-ticker pricing.

Would you like a comparison table of Bloomberg vs. these alternatives for REST API use?

From Blogger iPhone client

Cybersecurity

try hack me

https://youtube.com/shorts/W5vAMx4diK0?si=TjjMwYf-q4NrJMk8

From Blogger iPhone client

Spark vs Hadoop

Hadoop (MapReduce)

Uses disk-based batch processing
Slower due to frequent read/write to disk
More complex code (typically Java-based)
Good fault tolerance via HDFS replication
No native real-time processing (requires external tools like Apache Storm)
Machine learning via external tools like Apache Mahout
Best for: large-scale, cost-effective batch jobs

Apache Spark

Uses in-memory processing (much faster)
Supports batch, real-time, and streaming workloads
Easier to use with high-level APIs (Scala, Python, Java, R)
Efficient fault tolerance using RDD lineage
Built-in Spark Streaming and Structured Streaming
Includes MLlib for machine learning
Best for: fast, iterative tasks, real-time analytics, and machine learning

From Blogger iPhone client

ITIL applications

Here’s a list of popular ITIL (Information Technology Infrastructure Library) tools that help organizations manage IT services based on ITIL best practices. These tools typically support incident, problem, change, configuration, and service level management, among other ITSM functions.

Top ITIL-Compliant Tools

1. ServiceNow

Type: Cloud-based ITSM suite
Features: Incident, change, CMDB, knowledge management, workflows, automation
Strengths: Highly customizable, strong ecosystem, AI features

2. BMC Helix (Remedy)

Type: Enterprise-grade ITSM platform
Features: Full ITIL support, cognitive automation, AIOps, service desk
Strengths: Suitable for large enterprises, strong CMDB & integrations

3. Ivanti Neurons for ITSM

Type: Modular ITSM tool
Features: Incident, problem, change, asset management
Strengths: Low-code customization, flexible deployment (cloud/on-prem)

4. ManageEngine ServiceDesk Plus

Type: ITSM for SMBs and enterprises
Features: ITIL-ready modules, asset tracking, self-service portal
Strengths: Cost-effective, user-friendly

5. Jira Service Management (Atlassian)

Type: Agile-friendly ITSM
Features: Incident & change management, automation, integrations with Jira
Strengths: Great for DevOps and agile environments

6. Freshservice (by Freshworks)

Type: Cloud-native ITSM tool
Features: Service catalog, incident, problem, change management
Strengths: Simple UI, fast setup, good for mid-sized teams

7. Cherwell (now Ivanti)

Type: ITSM platform
Features: ITIL-aligned modules, flexible workflow engine
Strengths: Customizable without code, used in regulated industries

8. SolarWinds Service Desk

Type: ITSM + asset management
Features: Incident, change, SLA tracking, reporting
Strengths: Integrates well with SolarWinds monitoring tools

Open Source / Lightweight Alternatives

GLPI: Open-source ITSM suite with ITIL-aligned features
OTRS: Free version supports incident management and basic workflows
Snipe-IT: Focused on asset management, can be paired with ticket systems

Would you like a comparison table with pricing, ITIL support levels, or hosted vs. on-premise options?

From Blogger iPhone client

787 a check c check

Sure — a C-check for a Boeing 787-8 Dreamliner is a part of the aircraft’s scheduled maintenance program, typically performed every 20–24 months or after a set number of flight hours/cycles, depending on the airline’s maintenance planning document (MPD) and regulatory authority (e.g., FAA or EASA).

Here’s an outline of a typical C-check inspection checklist for a Boeing 787-8:

C-Check Inspection Overview – Boeing 787-8

1. Airframe Inspection

Fuselage: Structural inspection for cracks, dents, corrosion.
Doors & Windows: Latches, seals, hinges, and pressure integrity.
Wings & Empennage: Spars, ribs, skin panels.
Landing Gear: Struts, actuators, tires, brakes, and uplock mechanisms.

2. Flight Control Systems

Surfaces: Elevators, rudders, ailerons – hinges, linkages, and actuators.
Fly-by-wire system checks: Software versioning, signal verification.

3. Engines and Nacelles (GEnx or Trent 1000)

Engine mounts, fire detection systems, and pylon attachments.
Fan blades and internal borescope inspection.
Oil, hydraulic, and fuel leak inspection.

4. Electrical and Avionics Systems

Check power distribution units (PDUs) and Integrated Modular Avionics (IMA).
Verify communication, navigation, and surveillance systems.
Battery inspection (especially lithium-ion on 787s).

5. Environmental Control System (ECS)

Cabin air system, packs, bleed air systems.
Humidity control and cabin pressure monitoring.

6. Fuel System

Tanks, pumps, venting systems, and fuel quantity indication.
Inspections for microbial growth and seal integrity.

7. Hydraulics

Check for leakage, pressure stability, accumulator pre-charge.
Inspect flight control actuators and hydraulic power transfer units (PTUs).

8. Auxiliary Power Unit (APU)

Operational check and borescope inspection.
Check fire detection/suppression systems.

9. Cabin and Interior

Inspection and functional check of seats, lavatories, oxygen systems.
Smoke detectors, emergency lighting, and PA systems.

10. Software and Aircraft Health Monitoring

Updates to flight control and system management software.
Review health monitoring logs (e.g., Boeing Airplane Health Management system).

Documentation & Certification

Logbook entries and sign-offs by certified maintenance engineers.
Compliance with Airworthiness Directives (ADs) and Service Bulletins (SBs).
Update maintenance tracking systems (e.g., TRAX, AMOS, or Maintenix).

This is a generalized checklist — each airline customizes it based on their Approved Maintenance Program (AMP) and Boeing’s Maintenance Planning Data (MPD).

From Blogger iPhone client

Absolutely. An A-check is a lighter and more frequent maintenance check compared to the C-check. It’s part of the routine maintenance schedule for commercial aircraft, including the Boeing 787-8, and helps ensure airworthiness between more intensive checks.

A-Check Overview – Boeing 787-8

Frequency

Typically every 500–750 flight hours, or about 6–8 weeks, depending on the airline’s maintenance schedule and utilization.
Takes around 6–24 hours to complete.
Often done overnight at an airport maintenance base (line maintenance hangar).

Typical A-Check Task Categories

1. General Visual Inspections

Fuselage & Wing Surfaces: Exterior panels for dents, cracks, fluid leaks, missing fasteners.
Landing Gear & Tires: Wear, tire pressure, brake wear pins, hydraulic leaks.
Cargo Doors & Passenger Doors: Latches, seals, and proper operation.

2. Systems & Component Checks

Hydraulics: Reservoir levels, leaks, pressure checks.
Electrical Systems: Battery condition, generator load checks.
Avionics: Functional tests of nav, comm, and flight management systems.

3. Cabin & Emergency Equipment

Oxygen Systems: Bottle pressure, masks, and deployment mechanisms.
Fire Extinguishers & Emergency Lighting: Pressure, expiry, and functionality.
Seats, Belts, Lavatories: General condition and secure fittings.

4. Engines & APU

Fan Blade Visual Inspection: FOD (foreign object damage), nicks.
Oil Level Checks: Top-off as needed.
Filter Inspections: Bypass indicators and replacements if needed.

5. Software & Diagnostics

Check for error messages from the Aircraft Condition Monitoring System (ACMS) or Airplane Health Monitoring (AHM).
Update minor software packages if required (787s have extensive avionics software).

Typical Deliverables Post A-Check

Signed maintenance log entries.
Updates to the aircraft’s digital maintenance tracking system.
Rectification of any deferred defects (MEL/CDL items).

Tools & Systems Involved

Electronic Flight Bag (EFB) for recording tasks.
Maintenance software like TRAX, AMOS, or Maintenix.
Use of 787-specific GSE (Ground Support Equipment) and remote diagnostics tools.

Difference Between A-Check and C-Check

Building SSIS package

building an end-to-end SSIS (SQL Server Integration Services) data pipeline. This tutorial covers extracting data from a source, transforming it, and loading it into a destination — commonly known as ETL (Extract, Transform, Load).

End-to-End SSIS Data Pipeline Tutorial

Scenario

You want to extract customer data from a flat file (CSV), transform the data (e.g., clean and format), and load it into a SQL Server database.

Prerequisites

SQL Server and SQL Server Data Tools (SSDT) installed (Visual Studio with SSIS project template).
Sample source file (CSV).
A destination SQL Server database/table to load data into.

Step 1: Create an SSIS Project

Open Visual Studio.
Click Create a new project.
Select Integration Services Project, then click Next.
Name your project (e.g., CustomerETLPipeline) and choose a location.
Click Create.

Step 2: Create a Data Flow Task

In Solution Explorer, open Package.dtsx.
In the Control Flow tab, drag a Data Flow Task onto the canvas.
Double-click the Data Flow Task to switch to the Data Flow tab.

Step 3: Add a Source (CSV File)

In the Data Flow tab, drag a Flat File Source to the canvas.
Double-click it to configure:
Click New to define a flat file connection.
Browse to your CSV file and configure the column delimiter, header row, etc.
Click OK.

Step 4: Add Data Transformations (Optional)

Depending on your needs, drag any of the following:

Derived Column – to create new columns or transform data.
Data Conversion – to convert data types.
Conditional Split – to branch data flow based on conditions.
Lookup – to enrich data from another source.

Example:

Drag a Derived Column transform.
Connect it to the Flat File Source.
Double-click it to create expressions like trimming strings, calculating fields, etc.

Step 5: Add a Destination (SQL Server)

Drag an OLE DB Destination to the canvas.
Connect it to your transformation or source.
Double-click to configure:
Click New to create a new OLE DB Connection Manager.
Connect to your SQL Server database.
Choose the destination table or create a new one.
Map the input columns to destination columns.

Step 6: Execute and Test the Package

Press F5 to execute the package.
Monitor the progress via green/red icons.
Check the destination table in SQL Server to ensure data is loaded.

Step 7: Deploy the Package (Optional)

To schedule or run the package outside of Visual Studio:

Use Integration Services Catalog to deploy the package to SQL Server.
Use SQL Server Agent to schedule execution.

Best Practices

Use configurations or parameters for file paths and connection strings.
Log errors using SSIS logging.
Handle data quality issues during transformation.
Validate data types to avoid truncation or type mismatch errors.

Would you like this tutorial turned into a downloadable PDF, or should I tailor it to a more specific use case (e.g., API source, Excel, cloud DB)?

From Blogger iPhone client

Certainly! If you want SSIS to dynamically pick a file with a specific prefix (e.g., Customer_*.csv), you’ll need to use a Script Task in the Control Flow to search for that file and store its path in a variable, which the Flat File Connection Manager will then use.

Here’s how to insert this step into your pipeline:

[Updated Step] 2.1: Dynamically Pick a File with a Certain Prefix

Goal:

Pick a file from a directory with a specific prefix (e.g., Customer_*.csv), and pass its full path to the Flat File Connection Manager.

A. Add a SSIS Variable

Go to SSIS > Variables (or press Ctrl+K).
Create a new variable:
Name: SourceFilePath
Data Type: String
Value: Leave blank (to be filled dynamically)

B. Add a Script Task in Control Flow

In the Control Flow, drag a Script Task above your Data Flow Task.
Double-click it, and in the Script Task Editor:
Click Edit Script.
Use the following code in Main():

public void Main()

{

string folderPath = @"C:\Data\"; // Update this path

string prefix = "Customer_";

string[] files = Directory.GetFiles(folderPath, prefix + "*.csv");

if (files.Length > 0)

{

Dts.Variables["User::SourceFilePath"].Value = files[0];

Dts.TaskResult = (int)ScriptResults.Success;

}

else

{

Dts.TaskResult = (int)ScriptResults.Failure;

}

Here’s how to insert this step into your pipeline:

[Updated Step] 2.1: Dynamically Pick a File with a Certain Prefix

Goal:

Pick a file from a directory with a specific prefix (e.g., Customer_*.csv), and pass its full path to the Flat File Connection Manager.

A. Add a SSIS Variable

Go to SSIS > Variables (or press Ctrl+K).
Create a new variable:
Name: SourceFilePath
Data Type: String
Value: Leave blank (to be filled dynamically)

B. Add a Script Task in Control Flow

In the Control Flow, drag a Script Task above your Data Flow Task.
Double-click it, and in the Script Task Editor:
Click Edit Script.
Use the following code in Main():

By default, the example processes only the first matching file (files[0]). If there are multiple files with the same prefix (e.g., Customer_202504.csv, Customer_202505.csv), only the first one will be processed unless you modify the pipeline to loop through all matching files.

To Process Multiple Files: Use a

Foreach Loop Container

Here’s how you can enhance your SSIS package to process all files matching a prefix:

[New Step] Use Foreach Loop to Process Multiple Matching Files

1. Add a Variable

Create a string variable:

Name: SourceFilePath
Scope: Package level
Data Type: String

2. Add a Foreach Loop Container

In the Control Flow, drag a Foreach Loop Container.
Double-click it to configure:
Collection tab:
Enumerator: Foreach File Enumerator
Folder: C:\Data\ (your folder path)
Files: Customer_*.csv
Check “Fully qualified” file name
Variable Mappings tab:
Map Index 0 to the User::SourceFilePath variable

3. Move the Data Flow Task Inside the Loop

Drag your Data Flow Task inside the Foreach Loop Container so it runs once per file.

4. Set the Flat File Connection to Use the Variable

Open your Flat File Connection Manager.
Go to Properties > Expressions.
Set the ConnectionString property to:

@[User::SourceFilePath]

Now each time the loop iterates, SourceFilePath gets updated, and the connection manager reads the correct file.

Result

The pipeline will:

Iterate through all files with the prefix Customer_
Load each file using the Data Flow Task
Process them one at a time

Would you like me to generate a visual flow diagram for this setup?

Canadian

Airline

Fleet Size

Fleet Highlights

Air Canada

166

Mix of Airbus A220, A320 family, Boeing 737 MAX, 777, 787, and Embraer E175 aircraft

WestJet

138

Boeing 737 series, 787 Dreamliners, and Bombardier Q400s via WestJet Encore

Porter Airlines

29 De Havilland Dash 8-400 and 42 Embraer E195-E2 aircraft

Air Transat

All-Airbus fleet: A310, A321ceo, A321LR, and A330 aircraft

Sunwing Airlines

Boeing 737-800 and 737 MAX 8 aircraft

Flair Airlines

Boeing 737-800 and 737 MAX 8 aircraft

PAL Airlines

Dash 8-100/300, Saab 340, and Beechcraft 1900D aircraft

Pacific Coastal Airlines

Saab 340, Beechcraft 1900, and Grumman Goose aircraft

Calm Air

ATR 42/72 and Boeing 737 aircraft

Canadian North

Boeing 737-200/300/400, ATR 42/72, and Dash 8 aircraft

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Aircraft A Checks C Checks

Of course! Here’s a clear list of A-checks and C-checks typically done for an aircraft. I’ll make it structured and easy to follow:

A-Check (every ~400–600 flight hours or 200–300 cycles)

Light maintenance, usually done overnight or during short downtime.

General A-Check Items:

Visual inspection of fuselage, wings, landing gear.
Lubrication of key parts (hinges, actuators, etc.).
Operational checks of emergency equipment (oxygen bottles, fire extinguishers).
Cabin interior check (seats, belts, lavatory systems).
Inspection of fluid levels (oil, hydraulic fluid, potable water).
Tire and brake inspection.
Avionics system functional check.
Lighting systems check (internal and external).
Minor software updates (if applicable).
Fuel leak checks.
Drain water separators and sumps.
Replacement of filters (air conditioning, hydraulics).
Battery condition inspection and servicing.

C-Check (every ~20–24 months or specified flight hours/cycles)

Much deeper inspection, aircraft out of service for 1–2 weeks typically.

General C-Check Items:

Detailed structural inspection (using borescopes, non-destructive testing like ultrasonic or X-ray methods).
Comprehensive system checks (hydraulics, pneumatics, avionics, flight control systems).
Full landing gear inspection and servicing (may involve partial disassembly).
Interior refurbishment (carpets, panels, seats removal for inspection).
Corrosion inspection and treatment.
Electrical wiring checks and potential rewiring.
Pressure bulkhead and door seal inspections.
Engine and APU (Auxiliary Power Unit) inspections (sometimes off-wing checks).
Removal and inspection of flight control surfaces (rudder, elevators, ailerons).
Update or modification of airworthiness directives (ADs) or service bulletins (SBs).
Detailed fuel tank inspection (including opening of tank access panels).
Full operational tests of backup and redundant systems.
Replacement of life-limited parts nearing expiration.

Would you also like a simple table version you can use for easy tracking or reporting?

I can format it nicely for you if you want!

From Blogger iPhone client

YouTube to text

https://youtube.com/shorts/iQydw-MgwNI?si=2RNZvp7D-nIAJ2cd

From Blogger iPhone client

Drone analysis of aviation

The URL shown in your screenshot is:

https://donecle.com/products/#Iris_3D

This link takes you to the Donecle website, specifically the product section for Iris dentCHECK.

Would you also like me to give you a quick summary of what Iris dentCHECK does based on that page?

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Sidra IRB

Sidra Medicine’s Institutional Review Board (IRB) is a pivotal entity ensuring the ethical conduct of human subject research within the institution. It operates under the guidelines set forth by the Ministry of Public Health (MoPH) in Qatar and adheres to international standards such as the Belmont Report and the International Conference on Harmonization Good Clinical Practice (ICH-GCP) guidelines.

🔍 Overview of Sidra Medicine’s IRB

Ethical Oversight: The IRB is responsible for reviewing all human subject research conducted at Sidra Medicine, regardless of funding source, to ensure the protection of participants’ rights, dignity, welfare, and privacy.
Compliance: Sidra Medicine’s IRB policies and procedures align with MoPH’s regulations and international ethical standards, ensuring that research activities meet both local and global requirements.
Review Authority: The IRB has the authority to approve, require modifications in, or disapprove research activities involving human subjects. This includes initial reviews, continuing reviews, and the assessment of any changes to approved research protocols.
Informed Consent: A key aspect of the IRB’s function is to ensure that informed consent is obtained from all research participants or their legal guardians, with special considerations for vulnerable populations such as children.

📋 Submission and Approval Process

Protocol Submission: Researchers must submit their study protocols, informed consent documents, and any other relevant materials to the IRB for review.
Ethical Review: The IRB reviews submissions to assess the ethical implications, potential risks, and benefits to participants, ensuring that the study complies with ethical standards.
Approval: Upon satisfactory review, the IRB grants approval for the study to proceed. Researchers must obtain this approval before initiating any research activities involving human subjects.
Ongoing Oversight: The IRB conducts continuing reviews of approved studies, monitors compliance, and reviews any proposed changes to the research protocols.

📞 Contact Information

IRB Registration: Sidra Medicine’s IRB is registered with the MoPH under the code IRB-SIDR-2020-009, with the current registration valid until September 10, 2027.
IRB Chairperson: Dr. Ayman Saleh
Contact Email: asaleh2@sidra.org

For more detailed information on the IRB’s policies, submission guidelines, and procedures, please visit Sidra Medicine’s Clinical Research page: Sidra Medicine Clinical Research.

If you require assistance with the IRB submission process or have specific questions about conducting research at Sidra Medicine, feel free to ask!

From Blogger iPhone client

Dr. Ayman Saleh is a distinguished physician and researcher specializing in pediatric hematology, oncology, and bone marrow transplantation. He currently serves as the Division Chief of Pediatric Hematology-Oncology & Bone Marrow Transplant at Sidra Medicine in Doha, Qatar. In addition to his clinical leadership, Dr. Saleh holds the position of Chairman of the Institutional Review Board (IRB) at Sidra Medicine, where he oversees the ethical conduct of clinical research within the institution.

With over three decades of experience in pediatric medicine, Dr. Saleh has held multiple leadership roles throughout his career. Prior to joining Sidra Medicine in 2017, he served as the Division Chief of Pediatric Hematology Oncology from 1998 to 2011 and as the Medical Director and Chairman of the Department of Pediatrics from 2005 to 2007. His extensive experience includes chairing various committees such as the cancer care committee, credentialing committee, pediatric academic review committee, pediatric executive committee, standing committee of the board, and professional affairs committee.

Dr. Saleh is also an active researcher and has been a full member of the international Children’s Oncology Group (COG) since its inception in 2000. He has conducted numerous clinical trials and received multiple grants focusing on childhood cancer, hemophilia, hemoglobinopathies, and supportive care. His contributions to the field have been recognized with several awards, including the American Cancer Society Fellowship Award, Children’s Miracle Network Award, and the Hemophilia Grant Award funded by the Maternal and Child Health Bureau and the Centers for Disease Control and Prevention of the U.S.

For more detailed information about Dr. Ayman Saleh’s professional background and contributions, you can visit his profile on Sidra Medicine’s official website: .

Building CRO in Middle East

To operationalize a Contract Research Organization (CRO) in Qatar, it’s essential to engage with specific regulatory and institutional bodies that oversee clinical research activities. Below is a structured guide to the key entities and steps involved:

🏛️ Key Regulatory and Institutional Bodies in Qatar

Ministry of Public Health (MoPH) – Health Research Governance Department
Role: Primary regulatory authority for clinical research in Qatar. Responsible for approving clinical trials and ensuring compliance with ethical and scientific standards.
Requirements:
Obtain approval from the MoPH before initiating any clinical trial.
Ensure the research institution is accredited by the Health Research Governance Department.
Maintain a registered Institutional Review Board (IRB) under the Health Research Governance Department.
Application Process:
Submit an application form for clinical trial approval.
Provide an approval letter from the IRB.
Include a recommendation letter from the Data Safety and Monitoring Committee.
Attach the clinical trial protocol and informed consent form.
Reference:
Institutional Review Boards (IRBs) / Independent Ethics Committees (IECs)
Role: Review and approve clinical trial protocols to ensure the protection of human subjects.
Requirements:
All clinical trials must receive prior approval from a registered IRB/IEC.
Ensure compliance with ethical principles originating from the Declaration of Helsinki and Good Clinical Practice (GCP) guidelines.
Reference:
Hamad Medical Corporation (HMC)
Role: Qatar’s largest public healthcare provider and a significant site for clinical research.
Requirements:
Collaborate with HMC for access to clinical trial sites and patient populations.
Engage with HMC’s research administration for support and guidance on conducting research within their facilities.
Reference:
Sidra Medicine
Role: Specializes in pediatric and maternal health research.
Requirements:
Coordinate with Sidra’s IRB for studies conducted within their institution.
Focus on clinical trials that benefit children in the Arab region, particularly those with rare diseases.
Reference:

✅ Steps to Operationalize a CRO in Qatar

Institutional Accreditation
Ensure your CRO is accredited by the MoPH’s Health Research Governance Department.
Register your Institutional Review Board (IRB) under the same department.
Protocol Development
Design clinical trial protocols adhering to ICH-GCP guidelines and ethical standards.
Prepare comprehensive documentation, including informed consent forms and data management plans.
Ethical Approval
Submit the clinical trial protocol to a registered IRB/IEC for ethical review and approval.
Regulatory Submission
Apply for clinical trial approval through the MoPH, providing all necessary documentation and approvals.
Site Collaboration
Establish partnerships with institutions like HMC and Sidra Medicine for access to research sites and patient populations.
Compliance and Monitoring
Implement quality management systems to ensure ongoing compliance with regulatory requirements.
Conduct regular audits and monitoring of clinical trial activities.

By engaging with these key bodies and following the outlined steps, you can effectively establish and operate a CRO in Qatar, ensuring compliance with local regulations and contributing to the advancement of clinical research in the region.

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Building CRO

Building a Contract Research Organization (CRO) service requires a mix of scientific expertise, regulatory knowledge, operational efficiency, and client-centric business development. Here are the key elements to focus on:

⸻

1. Service Portfolio Design

• Core Services: Clinical trials (Phase I–IV), regulatory affairs, data management, biostatistics, medical writing, site monitoring.

• Specialized Services: Pharmacovigilance, bioanalytical lab services, patient recruitment, decentralized trials, etc.

• Therapeutic Focus: Oncology, neurology, rare diseases, etc., depending on your expertise.

⸻

2. Regulatory Compliance

• Adherence to ICH-GCP, FDA, EMA, and local health authority regulations.

• Ensure processes align with 21 CFR Part 11 (electronic records/signatures).

⸻

3. Talent & Expertise

• Hire clinical research professionals, project managers, data scientists, regulatory specialists, and medical experts.

• Emphasize continuous training and certifications (e.g., GCP, CRA, PMP).

⸻

4. Technology Infrastructure

• Clinical Trial Management System (CTMS)

• Electronic Data Capture (EDC)

• Randomization and Trial Supply Management (RTSM)

• AI-powered tools for patient recruitment, trial design, and remote monitoring.

⸻

5. Quality Management System (QMS)

• Develop SOPs for all functions.

• Implement audit trails, CAPA systems, and risk-based monitoring.

⸻

6. Site Network & Patient Access

• Build relationships with hospitals, clinics, and investigators.

• Consider strategic partnerships or alliances to access global sites.

⸻

7. Business Development & Partnerships

• Offer competitive pricing, flexible models (full-service or FSP).

• Build long-term relationships with sponsors—biotech, pharma, or academia.

⸻

8. Data & Security Infrastructure

• Ensure data integrity, HIPAA/GDPR compliance, and robust cybersecurity.

• Leverage real-world data and eSource technologies for speed and quality.

⸻

9. Global Reach & Localization

• If aiming for global trials, ensure regional regulatory expertise and multilingual teams.

• Understand cultural, logistic, and regulatory differences across geographies.

⸻

10. Reputation & Trust

• Demonstrate success via case studies, KOL endorsements, and trial performance metrics.

• Maintain transparency and ethical practices.

⸻

Would you like a sample org structure or roadmap to launch a CRO startup?

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Pharmaceutical and adoption of AI

Pharmaceutical companies are increasingly leveraging AI to accelerate clinical trials, improve success rates, and reduce costs. Here’s how they’re doing it:

Patient Recruitment & Matching

AI models analyze electronic health records (EHRs), genetic data, and demographics to identify eligible patients faster.
Natural language processing (NLP) helps extract relevant info from unstructured medical data.

Trial Design Optimization

AI simulates trial scenarios to help design smarter protocols—choosing endpoints, dosage, duration, and patient groups more effectively.
Machine learning can predict trial feasibility based on historical data and site performance.

Site Selection and Monitoring

AI predicts which trial sites will recruit and retain patients most effectively.
Real-time data monitoring with anomaly detection flags issues early (e.g., adverse events, protocol deviations).

Predictive Analytics for Outcomes

AI predicts patient response and early signs of efficacy or failure, allowing for faster go/no-go decisions.
Digital twins and predictive biomarkers help in adaptive trial design.

Decentralized Clinical Trials (DCT)

AI enables remote monitoring using wearables, mobile apps, and telemedicine.
Automates data collection and improves patient adherence and retention.

Natural Language Processing (NLP) for Data Extraction

Speeds up regulatory documentation, medical coding, and adverse event reporting.
Automates clinical trial result summarization and submission generation.

Would you like a real-world case study or visual summary of these applications?

From Blogger iPhone client

Several medications are employed to control bleeding in critical situations such as pregnancy-related hemorrhage and hemorrhagic strokes. Below is an overview of key drugs used in these scenarios, along with information about the pharmaceutical companies that produce them and their recent annual revenues.

Medications for Controlling Bleeding

Tranexamic Acid (TXA)

Use: Antifibrinolytic agent used to reduce bleeding in various conditions, including postpartum hemorrhage and trauma-related bleeding.
Manufacturers: Pfizer Inc., Teva Pharmaceutical Industries Ltd., and other generic drug manufacturers.

Oxytocin

Use: Hormone used to induce labor and control postpartum bleeding by stimulating uterine contractions.
Manufacturers: Novartis AG, Pfizer Inc., and various generic pharmaceutical companies.

Misoprostol

Use: Prostaglandin analog used to prevent and treat postpartum hemorrhage and for medical management of miscarriage.
Manufacturers: Pfizer Inc., Teva Pharmaceutical Industries Ltd., and other generic manufacturers.

Recombinant Activated Factor VII (rFVIIa)

Use: Promotes hemostasis in patients with hemophilia and is sometimes used off-label for severe bleeding, including intracerebral hemorrhage.
Manufacturer: Novo Nordisk A/S.

Desmopressin (DDAVP)

Use: Synthetic hormone used to treat bleeding disorders such as mild hemophilia A and von Willebrand disease by increasing levels of clotting factors.
Manufacturers: Ferring Pharmaceuticals, Teva Pharmaceutical Industries Ltd., and others.

Pharmaceutical Companies and Annual Revenues

Here are some of the major pharmaceutical companies producing these medications, along with their reported revenues:

Aviation industry and Bloomberg

Bloomberg Data Services is a suite of tools and APIs provided by Bloomberg that offer access to a wide range of financial, economic, and alternative datasets. These services are typically consumed through:

Bloomberg Terminal (Desktop access)
Bloomberg Data License (BDL)
Bloomberg REST API / SAPI / Python SDK
Managed services (feeds, reference data, pricing, etc.)

Types of Data Offered

Market data: Real-time and historical pricing for securities (equities, fixed income, FX, derivatives, commodities)
Reference data: Security master data, identifiers, corporate actions, and metadata
Economic data: Country-level indicators, inflation rates, GDP, employment stats
Alternative data: ESG scores, sentiment, satellite insights, etc.
FX and interest rate curves
News and financial events

How Airlines Can Benefit from Bloomberg Data

Even though Bloomberg primarily targets financial services, airlines and related industries can also gain significant value, especially in areas like fuel risk management, financial planning, and strategic decisions.

1. Fuel Hedging & Risk Management

Jet fuel prices and crude oil futures data help airlines monitor price volatility.
Use Bloomberg curves and real-time data to:
Build hedging strategies (swaps, futures)
Manage exposure to commodity price movements
Monitor FX impact on fuel purchases

2. Macroeconomic & Geopolitical Analysis

Access macroeconomic indicators to anticipate travel demand or route profitability:
Consumer confidence
GDP growth
Interest and inflation rates
Geopolitical news and risk signals to adapt operations (e.g., avoid volatile regions, anticipate regulatory changes)

3. FX Risk Management

Airlines operating across multiple regions can use:
Real-time FX rates
Historical trends
Volatility metrics
Helps in pricing tickets, settling contracts, and managing currency exposure

4. Competitor and Industry Benchmarking

Use equity and financial data to track:
Airline peers’ financial health (debt levels, stock performance, margins)
Industry benchmarks and valuation multiples
Useful for investor relations, M&A strategy, or pricing models

5. ESG and Carbon Emissions Data

Access to ESG scores, carbon metrics, and sustainability disclosures.
Helps align with regulatory or investor expectations on sustainability.

6. Financing & Treasury

Monitor interest rate curves, bond yields, and credit ratings for:
Issuing debt
Optimizing capital structure
Managing treasury investments

Summary

While Bloomberg may seem finance-focused, airlines can use its data services for:

Fuel and FX hedging
Economic forecasting
Competitor benchmarking
ESG reporting
Financing decisions

Want a tailored example, like how a specific airline (e.g., Qatar Airways or Delta) might integrate Bloomberg data into their strategy or systems?

From Blogger iPhone client

Bloomberg data pipeline benefits

Pulling data from the Bloomberg REST API can offer several powerful benefits, especially for financial institutions, investment firms, and enterprises that rely on timely and accurate market data. Here are the key advantages:

1. Real-time and Historical Market Data

Access real-time market data (price quotes, market depth, etc.) for equities, bonds, commodities, FX, and more.
Retrieve historical data for time series analysis, backtesting, or performance tracking.

2. Wide Coverage of Financial Instruments

Bloomberg supports a vast array of instruments including:
Equities
Fixed income
Derivatives
Currencies
Commodities
Economic indicators

3. Automation and Integration

Seamlessly integrate Bloomberg data into:
Analytics models
Dashboards (e.g., Power BI, Tableau)
Data warehouses/lakes
Trading systems and risk engines

4. Custom Queries and Flexible Data Retrieval

Use Bloomberg’s rich data model to customize requests (e.g., specific fields, conditions, filters).
Schedule pulls at regular intervals (e.g., end-of-day prices, intraday snapshots).

5. Improved Decision-Making

Get up-to-date and high-quality data to inform:
Portfolio strategies
Risk management
Market research
Compliance and reporting

6. Cost Efficiency and Performance

Reduce dependency on desktop terminals for data exports.
Increase speed and consistency of data updates without manual intervention.

7. Enhanced Governance and Auditability

API-based access can be monitored and logged for compliance.
Data lineage and quality controls become easier to implement and audit.

Would you like to dive into specific use cases like investment research, risk management, or data pipeline design with Bloomberg data?

From Blogger iPhone client

Phone analysis

https://youtube.com/shorts/ssAcCGotDEA?si=_Pe7gky92jvnLO7l

Dial

*#67#

to disable call forqarding

#002#

From Blogger iPhone client

IFS

IFS (Industrial and Financial Systems) offers a robust, aviation-specific software suite designed for airlines, MROs (Maintenance, Repair & Overhaul), and defense aviation operators. It is widely adopted by major global carriers and service providers for maintenance, engineering, asset, and supply chain management.

IFS Applications for Airlines

1. Maintenance, Repair & Overhaul (MRO)

Line, Base & Heavy Maintenance Support
Schedule, execute, and track all types of aircraft maintenance activities.
Component Maintenance
Manage overhaul, inspection, and test of rotable and repairable components.
Mobile MRO
Technicians access real-time work orders and manuals via tablets.

2. Fleet & Asset Management

Aircraft Configuration Management
Manage and track aircraft, engines, and component lifecycles.
Airworthiness & Compliance
Automate compliance with EASA, FAA, and other regulations; manage ADs and SBs.

3. Supply Chain Management

Inventory Optimization
Forecast demand, reduce excess stock, and ensure part availability.
Procurement & Logistics
Streamline purchase orders, vendor management, and parts logistics.

4. Maintenance Planning & Scheduling

Short- and Long-term Maintenance Planning
Balance aircraft availability with maintenance schedules.
Hangar & Resource Planning
Efficiently allocate bays, tools, and personnel.

5. Digital Transformation

AI & Predictive Maintenance
Integrate IoT sensors and AI algorithms for condition-based maintenance.
Integration with Flight Ops
Interfaces with flight planning, crew scheduling, and EFB systems.

IFS Airline Clients

IFS is used by a range of commercial and cargo airlines, such as:

LATAM Airlines Group
Qatar Airways
AirAsia Group
AAR Corp (as MRO provider)
Bristow Group
Cape Air
PSA Airlines (subsidiary of American Airlines)

Deployment Options

Cloud-based SaaS
On-premise installations
Hybrid models for data-sensitive operations

Would you like a comparison between IFS and other aviation systems like AMOS or AVIATAR? Or a breakdown of how it integrates with Oracle/SAP or BI tools?

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Aviator aviation

AVIATAR, developed by Lufthansa Technik, is a cloud-based digital platform designed to enhance airline operations by offering a suite of modular applications. These applications focus on predictive maintenance, real-time aircraft health monitoring, and streamlined technical operations. AVIATAR integrates data from various sources, including aircraft systems and maintenance records, to provide actionable insights that help airlines optimize their operations and reduce unscheduled interruptions.

✈️ Airlines Utilizing AVIATAR

Several airlines worldwide have adopted AVIATAR to improve their operational efficiency:

LATAM Airlines Group: Implemented AVIATAR across its Airbus A320, Boeing 777, and Boeing 787 fleets, totaling over 300 aircraft. The airline utilizes AVIATAR’s Predictive Health Analytics to transform data into actionable insights, optimizing technical operations and minimizing unexpected disruptions. Additionally, AVIATAR’s Electronic Technical Logbook digitizes cockpit-to-maintenance communication, improving efficiency.
United Airlines: Adopted AVIATAR for its Boeing 777 and Airbus A320 fleets, with plans to develop new predictive maintenance solutions for its Boeing 737 fleet. The integration aims to provide tech-ops professionals with data and recommendations to optimize operations and reduce maintenance-related delays.
ASL Aviation Holdings: ASL’s European airlines have adopted AVIATAR’s Electronic Technical Logbook solution to replace existing paper-based processes, enhancing efficiency and data accuracy.
Eurowings: Introduced AVIATAR’s Electronic Technical Logbook, which offers prefilled text blocks and automated input masks to capture technical issues during flight and on the ground, replacing manual entries and improving data quality.

🧠 Key Features of AVIATAR

Predictive Health Analytics (PHA): Utilizes live data from aircraft systems to predict potential technical issues before they occur, enhancing operational reliability and reducing unplanned maintenance events.
Electronic Technical Logbook (ETL): Digitizes the process of capturing technical issues, allowing for real-time data availability and seamless collaboration between pilots and maintenance teams.
Technical Repetitives Examination (TRE): An AI-based tool that analyzes recurring defects within aircraft technical logbook write-ups, making them more transparent and easier to monitor.
Engineering Analytics Suite: Part of AVIATAR’s Reliability Solutions, this suite enables users to create live data dashboards for fleet performance indicators, supporting root-cause analysis and authority reports.

AVIATAR’s modular design allows airlines to select and integrate applications that best fit their operational needs, contributing to improved efficiency, reduced costs, and enhanced safety in airline operations.

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Amos aviation

Swiss AviationSoftware’s AMOS (Aircraft Maintenance and Engineering System) is a comprehensive MRO (Maintenance, Repair, and Overhaul) software solution utilized by over 220 customers worldwide, including commercial airlines, cargo carriers, MRO providers, and aircraft manufacturers.

Notable AMOS Clients

Here are some of the prominent organizations currently using AMOS:

Air India – The flag carrier of India, operating a diverse fleet including Airbus A319, A320, A321, A350, and Boeing 777, 787.
Azerbaijan Airlines (AZAL) – The national airline of Azerbaijan, with aircraft such as Airbus A319, A320, A340, Boeing 767, 777, 787, and Embraer 190.
Flair Airlines – A Canadian low-cost carrier headquartered in Edmonton, Alberta, operating Boeing 737 aircraft.
airBaltic – The flag carrier of Latvia, operating Airbus A220 aircraft.
JetSmart Airlines – A South American ultra low-cost carrier based in Santiago, Chile, operating Airbus A320 and A321 aircraft.
Turkish Technic – The maintenance, repair, and overhaul (MRO) center of Turkish Airlines.
Saab AB – A Swedish aerospace and defense company, operating as both an aircraft manufacturer and MRO provider.
Magnetic Group – An aviation company and leading MRO service provider based in Tallinn, Estonia.
Asia Digital Engineering (ADE) – Part of Capital A, specializing in line and base maintenance, component & warehouse services, and engineering support services.
HAECO Global Engine Support – Provides global round-the-clock on-wing and off-wing engine support from strategic locations.

These clients represent a diverse range of aviation operations, showcasing AMOS’s adaptability to various organizational needs.

For a comprehensive list of AMOS customers, you can visit the Swiss-AS official customer list.

If you’re interested in information about AMOS clients in a specific region, such as North America or Europe, or within a particular segment like cargo or low-cost carriers, feel free to ask!

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IFS airline

IFS (Industrial and Financial Systems) offers an enterprise-grade aviation solution suite tailored for the aerospace and defense sectors, especially airlines, MRO providers, and military operators. It’s widely used for managing maintenance, engineering, supply chain, and operational efficiency.

Key Features of IFS for Airlines & Aviation

1. Aviation Maintenance Management

Line, Base & Heavy Maintenance
Schedule and manage all maintenance activities.
Real-time tracking of aircraft status and work packages.
Mobile Maintenance Support
Technicians can view and update work orders via tablets in the field or hangar.

2. Fleet & Asset Management

Aircraft Configuration Management
Track the entire lifecycle of aircraft, engines, and components.
Monitor airworthiness, service bulletins (SBs), and ADs (Airworthiness Directives).
Lifecycle Costing
Understand cost per flight hour and optimize asset investments.

3. Supply Chain & Inventory

Inventory Optimization
Forecast parts demand and reduce excess inventory.
Parts Tracking
Full traceability of parts and tools, with integration into logistics.
Warehouse Management
Integrated with barcoding and RFID for real-time part visibility.

4. Compliance & Safety

Regulatory Compliance
Compliant with EASA, FAA, and other global aviation authorities.
Maintenance Records
Complete digital audit trail and documentation for regulatory checks.

5. MRO and Third-Party Services

MRO Provider Module
For organizations offering maintenance as a service.
Includes quoting, invoicing, and customer contract management.

6. Planning & Optimization

Maintenance Planning
Long-term and short-term planning to minimize downtime.
Resource Management
Assign crews, tools, and bays efficiently.

Integration Capabilities

IFS can be integrated with:
ERP systems like Oracle, SAP, and MS Dynamics.
Flight operations systems for live aircraft status.
IoT/AI modules for predictive maintenance and performance analytics.

Airlines & Aviation Clients Using IFS

IFS powers systems for major airlines, defense forces, and MROs including:

LATAM Airlines
Qatar Airways
AAR Corp
Bristow Group

If you’re considering IFS for a new deployment or integration (like with Oracle Fusion or a BI stack), I can help map out the architecture or provide use-case specific guidance. Let me know how deep you’d like to go.

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Jeppesen

Jeppesen, a Boeing subsidiary, provides a wide range of digital solutions for the airline and aviation industries, with a strong focus on flight operations, crew management, navigation, and optimization. Here’s a breakdown of their core airline solutions:

1. Flight Planning & Dispatch

Jeppesen FliteDeck Pro: A widely used electronic flight bag (EFB) application for pilots, providing charts, NOTAMs, and real-time updates.
Jeppesen Flight Planning (JFP): Optimizes routes, fuel consumption, and cost by using advanced algorithms and real-time data.
Ops Control: A central platform to monitor and manage operations, from flight dispatch to real-time disruptions.

2. Crew & Operations Management

Crew Pairing and Rostering: Advanced optimization tools to build cost-effective crew schedules while ensuring compliance with labor agreements.
Crew Tracking: Real-time monitoring and reallocation of crew members during disruptions.
Fatigue Risk Management: Tools to evaluate and mitigate pilot fatigue based on scientific models and schedules.

3. Navigation Services

Airway Manual Services: Digital and paper-based charts and manuals used for global navigation.
Tailored Charts: Custom navigation charts specific to an airline’s operations or aircraft type.
Obstacle and Terrain Data Services: Used in flight planning and approach procedures.

4. Training and Qualification Management

Training Management Solutions: For tracking pilot and crew training, certification, and compliance.
Qualification Tracking: Ensures that only qualified staff are scheduled for operations, reducing compliance risk.

5. Digital Transformation & Optimization

Total Mission Optimization (TMO): Integrates data from flight planning, crew management, and maintenance to optimize the overall flight mission.
Aviator Suite: A connected, mobile-first suite for flight and ground crew, enhancing situational awareness and operational efficiency.

Jeppesen’s solutions are used by major global airlines, regional carriers, and military aviation units. Let me know if you’re looking for insights into one specific module (like crew management or flight planning), or how to integrate these with existing ERP or MRO systems.

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Robotics

Aircraft and aviation-related robots are widely used in both military and commercial sectors to enhance safety, efficiency, and productivity. Here are some notable types and use cases:

1. Aircraft Manufacturing Robots

Robotic Drilling & Fastening Systems: Used by Boeing and Airbus for precise assembly of fuselage and wings.
Composite Layup Robots: Automate the layering of composite materials, critical for lightweight aircraft parts.
Painting Robots: Ensure consistent coating while reducing human exposure to hazardous chemicals.

2. Maintenance, Repair, and Overhaul (MRO) Robots

Inspection Drones: Used for visual inspection of aircraft surfaces, including fuselage, wings, and tail.
Example: EasyJet’s drone for A-check inspections.
Ultrasonic Inspection Robots: Non-destructive testing using robotic arms to scan for cracks or corrosion.
Robotic Crawlers: Miniature robots that inspect hard-to-reach areas like fuel tanks and ducts.

3. Airport & Ground Operations Robots

Baggage Handling Robots: Automate luggage transport to improve efficiency and reduce delays.
Autonomous Tugs: Pull aircraft to and from gates without human drivers.
Example: TaxiBot, co-developed with Airbus and TLD.
Security Robots: Patrol terminals using facial recognition and anomaly detection.

4. In-flight Support and Cabin Service Robots

Service Robots: Provide food and beverages or assist with passenger needs.
Example: ANA’s “Newme” robot for telepresence assistance.
Cleaning Robots: Disinfect cabins between flights using UV-C light or automated spraying.

5. Research & Experimental Robots

Swarming UAVs: Tested for formation flying, surveillance, or wingman roles in military aviation.
Robotic Co-pilots: AI-based systems that support or replace human copilots in experimental aircraft.

If you’re exploring this for a project or specific use case (like MRO, AI integration, or cost analysis), I can dive deeper into those areas too.

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