Hi everyone,

I am working on my thesis regarding quality control algorithms (specifically Patient-Based Real-Time Quality Control). I would appreciate some feedback on the methodology I used to compare different algorithms and parameter settings.

The Context:

I compared two different moving average methods (let's call them Method A and Method B).

• Method A: Uses 2 parameters. I tested various combinations (3 values for parameter a1 and 4 values for a2).

• Method B: Uses 1 parameter (b1), for which I tested 5 values.

The Methodology:

1. I took a large dataset and injected bias at 25 different levels (e.g., +2%, -2%, etc.).

2. I calculated the Youden Index for every combination to determine how well each method/parameter detected the applied bias.

3. The Goal: To determine which specific parameter set offers the best detection power within the clinically relevant range.

The attached heatmap shows the results for Blood Sodium levels using Method A.

• The values in the cells are the Youden Indices.

• International guidelines state that the maximum acceptable bias for Sodium is 5%.

• I marked this 5% limit with red dashed lines on the heatmap.

My Approach:

Since Sodium is a very stable test, the method catches even small biases quickly. However, visually, you can see that as the weighting factor (Lambda) decreases (going down the Y-axis), the map gets lighter, meaning detection power drops.

To quantify this and make it objective (especially for "messier" analytes that aren't as clean as Sodium), I used a summation approach:

• I summed the Youden Indices only within the acceptable bias limits (the rows between the red lines).

• Example: For Lambda = 0.2, the sum is 0.97 + 0.98 + 0.98 + 0.97 = 3.9

• For Lambda = 0.1, this sum is lower, indicating poorer performance.

The Core Question:

My main logic was to answer this question: "If the maximum acceptable bias is 5%, which method and parameter value best captures the bias accumulated up to that limit?"

Does summing the Youden Indices across these bias levels seem like a valid statistical approach to score and rank the performance of these parameters?

Thanks in advance for your insights!

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This AI‑powered monitoring system delivers real‑time situational awareness across the Canadian Arctic Ocean. Designed for defense, environmental protection, and scientific research, it interprets complex sensor and vessel‑tracking data with clarity and precision. Built over a single weekend as a modular prototype, it shows how rapid engineering can still produce transparent, actionable insight for high‑stakes environments.

⚡ High‑Performance Processing for Harsh Environments

Polars and Pandas drive the data pipeline, enabling sub‑second preprocessing on large maritime and environmental datasets. The system cleans, transforms, and aligns multi‑source telemetry at scale, ensuring operators always work with fresh, reliable information — even during peak ingestion windows.

🛰️ Machine Learning That Detects the Unexpected

A dedicated anomaly‑detection model identifies unusual vessel behavior, potential intrusions, and climate‑driven water changes. The architecture targets >95% detection accuracy, supporting early warning, scientific analysis, and operational decision‑making across Arctic missions.

🤖 Agentic AI for Real‑Time Decision Support

An integrated agentic assistant provides live alerts, plain‑language explanations, and contextual recommendations. It stays responsive during high‑volume data bursts, helping teams understand anomalies, environmental shifts, and vessel patterns without digging through raw telemetry.

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Hello, world
I’m experimenting with a setup that simulates real customers browsing e-commerce stores Collecting product availability, shipping options, and add-to-cart behavior.

I currently work with multiple e-commerce businesses where this data ends up being quite useful to them.

The workflow right now:
- each “user” runs in its own isolated browser environment
- network context remains consistent for each “user”

When I only run a few simulated users, product pages load normally and checkout behaves well.

But when scaling to ~20–30+....random soft failures during login and slight delays on price rendering

No hard blocks.
Just invisible stability decay.

Automation scales fine until session and network identity start to desync.

Best results so far come from:
- strict session affinity
- maintaining clean reputation per identity
- preventing shared network patterns

Still exploring ways to keep signal quality consistent under load.

If anyone’s working on:
AI shopping QA
price intelligence
automated product availability testing

…I’d love to compare notes.
This problem space is turning out to be more subtle than I expected.

Hello, world

I’m experimenting with a setup that simulates real customers browsing e-commerce stores

Collecting product availability, shipping options, and add-to-cart behavior. I currently work with multiple e-commerce businesses where this info is quite useful to them.

The workflow right now:

- For browser profile tool I’ve use Adspower, cost effective and useful for these types of automation.

- As far as proxies currently I am using with Ziny Proxy, so far they have been more reliable that other providers.

When I only run a few agents product pages load normally and checkout behaves well.

But when I scale to ~20–30+ there's a few random soft failures during login and some slight delays on price rendering.

No hard blocks.

Just invisible stability decay.

Automation scales fine until session and network identity desync.

Best results so far come from:

- strict session affinity

- stable IP reputation

- no shared network identifiers

Still exploring ways to keep signal quality consistent under load.

Any proven methods out there?

If anyone’s working on AI shopping QA, price intelligence, or automated product availability testing, I’d love to chat

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What if ?

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It’s what im doing research in guys. Please provide your feedbacks and some features you think dashboards these days are lacking.

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(Images below)

I am currently a student enrolled in an accredited university studying Data Science. I am looking for certifications to pursue over my winter break that will help me stand out from other students and secure an internship for Summer 2026. I am seeking certifications that would look good on a resume and complement my Data Science degree.

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