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Assertible
Assertible automated QA tools test and monitor your web services across deployments and environments. These API tools provide assertions to test endpoints and sync tests with API changes.

Features:

Schedule tests to run automatically at specific intervals or continuous integration workflows.
This tool uses dynamic variables to manage and customize API requests, including environment-specific values and response data.
To test interactions, simulate API responses with mock endpoints without depending on live APIs.
Integrates with tools to execute web app tests when pushing code to GitHub or send alerts to Slack if failures happen.
This tool provides test reports.
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ACCELQ offers API chaining and regression suite capabilities for mature API testing. This test automation platform achieves end-to-end validations with API and UI in the same flow. API testing with this platform brings regression maturity. You can easily reuse and chain your API tests for integrated automation. Most importantly, programming is not required to build the API regression suite. ACCELQ ensures 360° quality test coverage by seamlessly embedding critical server-side API validations and your front-end testing.

Features:

ACCELQ includes API verifications with a simple and natural interface.
REST, SOAP, and custom protocols are supported for complete API testing.
Codeless to automate API tests on the Cloud in the same simplified flow.
ACCELQ supports API test case management, test planning, execution, and tracking governance.
In-sprint automation with seamless API automation is supported.
ACCELQ chains API tests for true end-to-end validation.
Supports integrated CI workflow to trigger automated API suite regression.
ACCELQ supports simple and automated change impact analysis of the API test suite.
Execution tracking of API tests with full visibility and defect-tracking integrations are supported.
Dynamic live results view with actionable reports to trigger reruns.
ACCELQ restricts application access via Oauth 2.0-based security and tenant group access policies.

The IoT-Based Smart Farming Cycle
The core of IoT is the data you can draw from things and transmit over the internet. To optimize the farming process, IoT devices installed on a farm should collect and process data in a repetitive cycle that enables farmers to react quickly to emerging issues and changes in ambient conditions. Smart farming follows a cycle similar to this one:

1. Observation . Sensors record observational data from the crops, livestock, soil, or atmosphere.

2. Diagnostics. The sensor values are fed to a cloud-hosted IoT platform with predefined decision rules and models—also called "business logic"—that ascertain the condition of the examined object and identify any deficiencies or needs.

3. Decisions . The user and/or the machine learning-driven components of the IoT platform assess the revealed issues to decide if location-specific treatment is necessary.

4. Action . After end-user evaluation and action, the cycle repeats from the beginning.

When to contact a doctor
A person should seek medical help if they have back pain:

that does not improve with rest
after an injury or fall
with weakness
with tingling or “pins and needles”
with unexplained weight loss
If any of the following occur alongside the pain, seek medical attention immediately:

fever
incontinence
sudden difficulty urinating or having bowel movements
numbness anywhere in the body
a lump or swelling on the back

The IoT-Based Smart Farming Cycle
The core of IoT is the data you can draw from things and transmit over the internet. To optimize the farming process, IoT devices installed on a farm should collect and process data in a repetitive cycle that enables farmers to react quickly to emerging issues and changes in ambient conditions. Smart farming follows a cycle similar to this one:

1. Observation . Sensors record observational data from the crops, livestock, soil, or atmosphere.

2. Diagnostics. The sensor values are fed to a cloud-hosted IoT platform with predefined decision rules and models—also called "business logic"—that ascertain the condition of the examined object and identify any deficiencies or needs.

3. Decisions . The user and/or the machine learning-driven components of the IoT platform assess the revealed issues to decide if location-specific treatment is necessary.

4. Action . After end-user evaluation and action, the cycle repeats from the beginning.

Smoke test: The Internet of Things (IoT) has provided ways to improve nearly every industry imaginable. In agriculture, IoT has not only provided solutions to often time-consuming and tedious tasks but is totally changing the way we think about agriculture. What exactly is a smart farm, though? Here is a rundown of what smart farming is and how it's changing agriculture.

What is a Smart Farm?
Smart farming refers to managing farms using modern Information and communication technologies to increase the quantity and quality of products while optimizing the human labor required.

Among the technologies available for present-day farmers are:

Sensors: soil, water, light, humidity, temperature management
Software:  specialized software solutions that target specific farm types or applications agnostic IoT platforms
Connectivity: cellular, LoRa
Location: GPS, Satellite
Robotics: Autonomous tractors, processing facilities
Data analytics: standalone analytics solutions, data pipelines for downstream solutions -- edited

Smoke test -- A management information system (MIS) is a system that collects a company's data and uses it to make more nimble, informed, and impactful business decisions. It's also an academic discipline you can study if you're interested in this type of work. If you enjoy using technology to solve business problems or answer important business questions, then a career in MIS may be a good fit.

Learn more about the benefits of a management information system, key skills you'll need to succeed, and career paths you can pursue. Afterward, if you're interested in building important business

AI in Qualitative Research: ChatGPT vs. Human Coders
An MSU study examined ChatGPT’s role in qualitative data analysis, comparing AI-augmented and human coding of hotel guest experiences. AI-generated themes aligned with human-coded ones but missed social interactions and safety concerns. A hybrid approach—AI for initial coding with human refinement—balances efficiency and analytical rigor.