archimedes experiment lab report

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Archimedes’ principle.

In this lab, the buoyant force on an object is measured by taking the difference between the object's weight in air, and its apparent weight in water. This measured buoyant force is compared to the theoretical value calculated using the object's volume, and Archimedes' Principle.

Several objects of different shapes are examined: Some of the objects have the same density, some have the same volume, and some have the same mass. The dependence of the buoyant force on density, mass, volume and shape is explored.

Grade Level: College • High School

Subject: Physics

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Archimedes Principle Lab Manual, Lab Reports of Physics

Its basic purpose is to study buoyant force as a function of submersed volume and also to use to determine the densities of a solid sample and a liquid sample.

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Determining Density Using Archimedes' Principle in Physics Lab

Experiment and test AI models with Podman AI Lab

Discover how Podman AI Lab's Playground feature accelerates your AI application development workflow

Experimenting with AI models and testing different prompts is a key part of developing AI-enabled applications. However, setting up the right environment and managing model dependencies can be challenging.  Podman AI Lab 's Playground feature solves this by providing an intuitive, containerized environment for exploring open-source machine learning models locally. Let’s explore the Playground feature in-depth and show how it can accelerate your application development workflow when working with generative AI.

What is Podman AI Lab?

Podman AI Lab is an extension for  Podman Desktop , the graphical tool for managing containers and Kubernetes , and enables developers to work with  large language models (LLMs) and other AI models locally on their machine. It provides an intuitive interface to discover models, spin up playground environments for experimentation, serve models via REST APIs, and use pre-built "recipes" that showcase common AI use cases and best practices. Figure 1 shows the first screen you will see upon opening the extension.

Podman AI Lab runs the open source models in  containers using Podman's container engine under the hood. This makes it easy to get started with AI without worrying about library conflicts, driver issues, or resource contention on your local machine. Since it's built on open source, you can examine how the models and example applications work under the covers (Figure 2).

Experimenting and testing AI models

Let's set up a playground environment on our local machine so we can test and compare AI models with prompts, benchmarks, and more. We’ll explore the capabilities of curated  HuggingFace models, import custom models, tune performance, and integrate AI with your applications.

Setting up Podman AI Lab and downloading models

To get started, first install Podman Desktop from  podman-desktop.io if you haven't already. Launch Podman Desktop, click the Extensions icon on the left sidebar, and install the Podman AI Lab extension (Figure 3).

When you open Podman AI Lab, Navigate to the Catalog , which contains a curated set of powerful open-source models that Podman AI Lab makes easy to use (see Figure 4). It includes both foundational models that can be applied to many use cases (like  Mistral and  Granite ) as well as more specialized models for tasks like image generation and audio transcription.

It’s also important to understand how a model can be used & redistributed, and by selecting the model itself, you can view useful details like the model provider, license, and capabilities, as shown in Figure 5.

As an example, let’s download TheBloke/Mistral-7B-Instruct-v0.2-GGUF , based on Mistral, and while only trained with 7 billion parameters, is a well-performing reasoning model that could fit various use cases. See Figure 6.

The catalog provides many models that are already compatible with  Podman AI Lab’s Recipies (sample applications to get started with chatbots, summarizers, code generation, object detection, etc.) on your local machine. You may want to consider importing your own models that you’ve discovered (perhaps from the  Open LLM Leaderboard on HuggingFace ) or fine-tuned using an approach like  InstructLab . From the Models Catalog , you can easily import a quantized .gguf file to the catalog and make it available for use in Podman AI Lab, as shown in Figure 7.

Experimenting with a playground environment

To interactively experiment with a model and try out different prompts, let’s launch a Playground environment. Navigate to Playgrounds from the left sidebar, where we can select New Playground and specify the name of our new environment and a downloaded model to use (Figure 8).

Podman AI Lab will spin up a containerized model server and interactive interface for sending queries and seeing results. The Playground runs the model and chat application in an isolated Podman Pod, abstracting away infrastructure complexity, and letting you easily inference the model based on the OpenAI-compatible API. See Figure 9.

Exploring model capabilities

With a Playground launched, let’s start exploring what the model can do. Enter natural language queries or prompts and see how the model responds. Test different types of tasks, from questions to open-ended generation, to gauge the model's strengths and weaknesses, as shown in Figure 10.

You can use "system prompts" to constrain the model's behavior to align with your application's needs. For example, give the model a persona such as "You are a helpful customer support agent. Answer questions concisely and professionally." See Figure 11.

The Playground logs your conversation, so you can track what works well. Iterate to refine your prompts for accuracy, safety, and relevance.

Tuning model performance 

The Playground allows you to tune the model's behavior through several configuration options, as Figure 12 depicts.

Temperature: Controls the randomness of responses. Lower values are more focused, higher values are more creative.

Max Tokens: Sets the maximum length of the model's output, influencing verbosity and resource consumption.

Top-p: Adjusts the balance between relevance and diversity in word choices.

Experiment with these settings interactively to find the optimal configuration for your use case. You'll notice there are tradeoffs between predictability and creativity, as well as conciseness and comprehensiveness.

Comparing models

You can work with multiple Playground instances concurrently to compare different models on the same prompts. Evaluate the models on criteria like response quality, speed, and the size of the model. This "bake-off" can help to determine which model is the best fit for your application.

Integrating with your application

Once you've refined the model choice, prompts, and configuration, you're ready to supercharge your application with generative AI. Podman AI Lab enables you to serve the model as a containerized REST endpoint that your code can call, just like any other API. From the Services tab, select New Model Service to specify the model and port to expose (Figure 13).

The Model Serving view provides the endpoint URL along with code snippets in popular languages for making requests. Use these as a starting point to integrate the model's capabilities into your application backend. The API is compatible with the  OpenAI format , so you can easily swap between local and hosted models. See Figure 14.

Let’s say we’re working on a banking application built with  Quarkus , the Kubernetes-native Java stack for developer joy. We can easily integrate the generative AI model into my application by adding the new dependencies for  LangChain4j and a connection to my served model to the application.properties . Now, we can create a service to interact with the LLM through a @RegisterAiService annotation, and call that method in other resources ( more about Quarkus and Langchain4j here )! Figure 15 depicts this service.

Podman Desktop manages the model server container, ensuring high availability and efficient resource utilization. You can monitor its performance and logs through the Podman Desktop dashboard. Since it runs locally, you keep full control of your data and intellectual property. See Figure 16.

Podman AI Lab 's Playground provides an intuitive, powerful environment for exploring open source AI models and testing different approaches. It abstracts away infrastructure complexity and allows you to focus on integrating AI into your applications. You can evaluate various models from the curated catalog (or import your own models), tune configurations for performance and results, and easily serve the model to an API endpoint for usage in your application, no matter the language or framework.

Try out the Podman AI Lab today, an extension of the  Podman Desktop project for working with containers and Kubernetes.

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Watch CBS News

Infectious bird flu survived milk pasteurization in lab tests, study finds. Here's what to know.

By Alexander Tin

Edited By Paula Cohen

Updated on: June 14, 2024 / 2:59 PM EDT / CBS News

A "small but detectable quantity" of infectious H5N1 bird flu virus was able to survive a common approach to pasteurizing milk, according to new research co-authored by scientists at the National Institutes of Health. 

The findings, published Friday in The New England Journal of Medicine, were based on experiments run at the agency's lab. The researchers note this is not the same as finding infectious H5N1 virus in milk from grocery stores. 

So far, officials have not detected infectious virus in any supermarket milk samples.

The finding comes as authorities are still identifying new infected herds in this year's unprecedented outbreak of H5N1 bird flu in dairy cattle. 

Infections have been detected in cows across farms in at least a dozen states, with most of the positive tests coming from raw milk samples that were teeming with the virus. Authorities have called on states to curb raw milk sales that could spread the virus, and have warned consumers against drinking raw milk .

"The study reflected experimental conditions, and should not be used to draw any conclusions about the safety of the U.S. milk supply," a Food and Drug Administration spokesperson said in a statement.

In the real world of commercial dairy processing, milk from infectious cows is likely being mixed with milk from healthy cows, diluting the virus and making it less likely there would be enough of it to survive. Technical details of how the milk is pasteurized, as well as additional steps to treat the milk, also cut the risk. Pasteurization involves treating the milk at high temperatures for a period of time to kill contamination.

Results from previous FDA studies of 297 samples of retail dairy products like milk and yogurt did not turn up any infectious virus. Earlier tests found only some harmless fragments of the virus leftover  from pasteurization.

Experimental laboratory conditions

"These are more or less experimental laboratory conditions. And we think that mechanical pasteurization in dairy farming will probably be more effective than what we do," said Vincent Munster, chief of the virus ecology unit at the National Institute of Allergy and Infectious Diseases.

Munster's lab in Montana has done work for years studying the ability of viruses to stay infectious in various conditions, including previous work pasteurizing pathogens in milk, like from an outbreak of coronaviruses in camels from Jordan . 

In this study, they looked at two approaches of pasteurization, tested on milk with concentrations of H5N1 they added at levels similar to what has been seen from some highly infectious cows. 

One pasteurization method they looked at heated milk to 72 degrees Celsius (about 161 degrees Fahrenheit) for 15 seconds, similar to the "high temperature short time" method that is used widely across the dairy industry. That yielded milk with what Munster said still had "minute amounts of infectious virus."

"You're really talking about like 10 virus particles, whereas the initial starting dose would be something like 10 million or 100 million virus particles," said Munster.

He also pointed to differences between the lab study and real-world industry practices which give commercial milk pasteurization a step up against the virus.

"There is an initial period in the dairy milk pasteurization that the milk needs to get from let's say 4 degrees to 72 degrees [Celsius]. And obviously once it starts hitting around 56 degrees, it already starts inactivating the virus," he said.

The second pasteurization approach — a half hour at 63 degrees Celsius — was more effective. Infectious virus was undetectable within minutes, long before pasteurization was over.

"Pasteurization methods were developed to actually reduce the amount of viable bacteria in milk to prolong fridge life, so to speak. And they're not necessarily initially designed to inactivate viruses," said Munster.

Pasteurizing milk longer or at hotter temperatures

Munster thinks that even adding 5 to 10 more seconds of pasteurization could offer the dairy industry a "safety buffer," ensuring that there's no active virus leftover in milk even if their raw milk supply turns out to have greater concentrations of infectious virus than the lab's .

"If you really want to make 100% sure there's no active virus, increasing the duration even by like 5 to 10 seconds of pasteurization would allow you to actually increase that safety margin," Munster said.

But an FDA spokesperson said that their testing data so far shows the pasteurization processes used by U.S. dairy companies are effective at killing H5N1. Many companies "use temperatures that are greater, often much greater than the minimum standards," the spokesperson said, and equipment that more consistently heats milk.

"[T]he United States would hesitate to change pasteurization parameters without data to demonstrate a public health need," the spokesperson said, warning that changing the standards would affect the flavor of dairy products.

The agency has so far not released results from its own study to validate the pasteurization of raw milk for H5N1, first announced earlier this year. Last month, it cited "the totality of the evidence" in reiterating that "the commercial milk supply is safe."

The spokesperson said the FDA's study "is a top priority for the agency" and that they were working to share the results in the near future. The agency is looking to validate "real-world processing conditions" with equipment used in commercial facilities.

"Sound science is critical to informing public health decisions like those made by the FDA related to food safety and we take this current situation and the safety of the milk supply very seriously," the spokesperson said.

Munster said the FDA and the U.S. Department of Agriculture also have studies looking at pasteurization, among a number of groups that have confirmed heating up milk is often able to fully inactivate the threat from H5N1 virus.

"Fortunately, they do actually inactivate viruses very well. But I think the focus point should be two fold: making sure that pasteurization is up to the task we're asking from it, and the data suggests that it is, but also that we should minimize any H5N1 positive milk actually coming into these dairy pasteurization," he said.

• Avian Influenza

Alexander Tin is a digital reporter for CBS News based in the Washington, D.C. bureau. He covers the Biden administration's public health agencies, including the federal response to infectious disease outbreaks like COVID-19.

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