Podcasts about Classification

Dans cet épisode, nous allons évoquer une des conséquences de la directive européenne en matière de transparence des rémunérations.

Commentary by Dr.  Jian'an Wang.

Types of GamesToday, we're diving into something that might seem simple on the surface, but as it turns out, it's anything but. In this episode, we're going to be talking about game types, and not just your usual categories like board games or video games. We're digging deeper: into the psychology, the structure, and the social impact of how we play. So let's get ready to explore the worlds of competitive, cooperative, and hybrid games—and why defining different game types isn't nearly as straightforward as it sounds.If you liked this episode please consider commenting, sharing, and subscribing.Subscribing is absolutely free and ensures that you'll get the next episode of Experience Points delivered directly to you.I'd also love it if you took some time to rate the show!I live to lift others with learning.  So, if you found this episode useful, consider sharing it with someone who could benefit.Also make sure to visit University XP online at www.universityxp.comUniversity XP is also on Twitter @University_XP and on Facebook and LinkedIn as University XPAlso, feel free to email me anytime at dave@universityxp.comGame on!Get the full transcript and references for this episode here: https://www.universityxp.com/podcast/145Support the show

This week we're covering entity identification numbers and classification changes.

Listen in as Scott Cline sits down with Monica Joseph-LaTange with Gusto! This is the start of a great series regarding important HR and Payroll topics with expertise provided by Gusto!Monica provides insightful details about how to properly classify your team members when it comes to payroll compliance. Questions answered in this episode include: Should my team member be classified as an employee or independent contractor? What is the difference between exempt employees and non-exempt employees? What are some pitfalls employers need to avoid when it comes to classification of employees? Please reach out to info@acquiosalliance.com to connect with Gusto for HR and payroll needs. Please note, any information provided within this episode should not be taken or construed as legal advice. 

Vous pouvez joindre Joueurs info service au 09 74 75 13 13, de 8h à 2h, 7 jours sur 7. Votre appel est anonyme et non surtaxé (coût d'une communication locale depuis un poste fixe ou inclus dans les forfaits des box et des mobiles).Que vous soyez concerné directement ou indirectement par un problème de jeu excessif, n'hésitez pas à appeler Joueurs info service, vous y trouverez :Des professionnels formés aux difficultés rencontrées avec la pratique de jeu excessifUne écoute sans jugement et confidentielleDes informations précises, une aide personnaliséeDes orientations adaptées à votre situationHébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

When the American Ornithological Society's Committee on Classification and Nomenclature announces their annual changes to the official Checklist, birders take notice!

In this hour, WPIAL Karlo Zovko comes on to break down the beginning of high school football season with Dorin Dickerson and Pat Bostick! Also, another edition of QUIZ CALLAS! August 21, 2025, 8:00 Hour

Andy Schlafly of Phyllis Schlafly Eagles Trump Reclassifying Marijuana Would Be A Huge Political Blunder The post President Trump and the Possible Downgrading of the Federal Classification on Marijuana – Andy Schlafly, 8/20/25 (2322) first appeared on Issues, Etc..

In this episode, we dive into the industry adoption for the new NMFTA's LTL freight classification. Our guest, Joe Ohr, COO at the NMFTA breaks down the changes, the adoption and some of the nitty gritty details that comes with implementing industry-changing tech. For more information, subscribe to ⁠Check Call the newsletter⁠ or the podcast. ⁠Follow the Check Call Podcast⁠ ⁠Other FreightWaves Shows⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, we dive into the industry adoption for the new NMFTA's LTL freight classification. Our guest, Joe Ohr, COO at the NMFTA breaks down the changes, the adoption and some of the nitty gritty details that comes with implementing industry-changing tech. For more information, subscribe to Check Call the newsletter or the podcast. Follow the Check Call Podcast Other FreightWaves Shows Learn more about your ad choices. Visit megaphone.fm/adchoices

The FCA recently published new guidance specifically in relation to UK PEPs which we decided needed a closer look. So we did!Send us a textSupport the showFollow us on LinkedIn at https://www.linkedin.com/company/the-dark-money-files-ltd/ on Twitter at https://twitter.com/dark_files or see our website at https://www.thedarkmoneyfiles.com/

Send us a textIn this episode of PTs Snacks Podcast, host we dive deep into the topic of spondylolythesis, discussing what it is, the different types, how to evaluate it, and typical treatment approaches. We explore several classifications, including dysplastic, isthmic, degenerative, traumatic, and pathologic types, and explains common symptoms and patient presentations. We also covers diagnostic tools such as radiographs and MRIs, and outlines treatment and management strategies, stressing the importance of creating a healing environment and referring patients to specialists when necessary. 00:00 Introduction to PTs Snacks Podcast00:21 Understanding Spondylolysis02:57 Types of Spondylolysis06:02 Diagnostic Imaging for Spondylolysis07:23 Treatment Approaches for Spondylolysis08:56 When to Consider Surgical Intervention10:21 Conclusion and Additional ResourcesSupport the showWhy PT Snacks Podcast?This podcast is your go-to for bite-sized, practical info designed for busy, overwhelmed Physical Therapists and students who want to build confidence in their foundational knowledge without sacrificing life's other priorities. Stay Connected! Never miss an episode—hit follow now! Got questions? Email me at ptsnackspodcast@gmail.com or leave feedback HERE. Join the email list HERE On Instagram? Find unique content at @dr.kasey.hankins! Need CEUs Fast?Time and resources short? Medbridge has you covered: Get over $100 off a subscription with code PTSNACKSPODCAST: Medbridge Students: Save $75 off a student subscription with code PTSNACKSPODCASTSTUDENT—a full year of unlimited access for less!(These are affiliate links, but I only recommend Medbridge because it's genuinely valuable.) Optimize Your Patient Care with Tindeq: Get 10% off with code PTSNACKS10: [Tindeq] ...

The Complexities of Homicide Law and Its Classifications ⚖️Homicide, the killing of one human being by another, is one of the most serious crimes in any legal system. However, not all homicides are treated equally. The law recognizes a complex spectrum of culpability, ranging from justifiable acts to the most heinous murders. Understanding these distinctions is crucial, as the classification of a homicide directly impacts the charges, potential defenses, and sentences an individual may face.Homicide vs. Murder vs. ManslaughterIt's important to first clarify some key terms:Homicide is the broadest term, simply meaning the killing of a human being. This includes both criminal and non-criminal acts.Criminal Homicide refers to a killing without justification or excuse. It's further divided into murder and manslaughter.Murder is a criminal homicide committed with malice aforethought, a legal term that essentially means a premeditated or reckless disregard for human life.Manslaughter is a criminal homicide committed without malice aforethought.The Classification of MurderMurder is typically divided into degrees to reflect the level of intent and premeditation.First-Degree Murder: This is generally defined as an intentional killing that is premeditated and deliberate. It's the most serious form of murder, often carrying the harshest penalties, including life imprisonment or the death penalty in some jurisdictions. Some states also include felony murder in this category, where a death occurs during the commission of a dangerous felony like robbery or arson, even if the killing wasn't intentional.Second-Degree Murder: This classification typically involves an intentional killing that is not premeditated. It can also include killings caused by a person's reckless actions that demonstrate a depraved indifference to human life, even if there was no intent to kill. For example, shooting a gun into a crowd and killing someone, without a specific target in mind, would likely fall under second-degree murder.The Classifications of ManslaughterManslaughter is a less severe form of criminal homicide because it lacks the element of malice aforethought.Voluntary Manslaughter: This occurs when an intentional killing is committed in the "heat of passion". The key is that the killing was provoked by something that would cause a reasonable person to lose control, and there was no time for the person's emotions to cool down. An example might be finding your spouse in bed with another person and immediately killing one of them in a fit of rage.Involuntary Manslaughter: This is an unintentional killing resulting from recklessness or criminal negligence. This typically happens when a person's actions, while not intended to cause death, show a disregard for the safety of others. Driving drunk and causing a fatal accident is a common example of involuntary manslaughter.Non-Criminal Homicide: Justifiable and ExcusableNot all killings are criminal. The law recognizes certain situations where a homicide is considered justifiable or excusable.Justifiable Homicide is a killing that is legally sanctioned. Examples include a police officer killing a dangerous felon to prevent a crime, or a soldier killing an enemy combatant in wartime.Excusable Homicide is a killing committed by someone who is not criminally at fault. The most common example is a killing in self-defense where the person had a reasonable fear of imminent harm or death and used a proportional amount of force to protect themselves.The complexities of homicide law reflect the deep moral and ethical questions society faces when one person takes the life of another. The legal system, through its various classifications, attempts to provide a framework for accountability that is both just and proportional to the offender's intent and actions.

Join hosts Lois Houston and Nikita Abraham, along with Principal AI/ML Instructor Himanshu Raj, as they dive deeper into the world of artificial intelligence, analyzing the types of machine learning. They also discuss deep learning, including how it works, its applications, and its advantages and challenges. From chatbot assistants to speech-to-text systems and image recognition, they explore how deep learning is powering the tools we use today.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast. I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! Last week, we went through the basics of artificial intelligence. If you missed it, I really recommend listening to that episode before you start this one. Today, we're going to explore some foundational AI concepts, starting with machine learning. After that, we'll discuss the two main machine learning models: supervised learning and unsupervised learning. And we'll close with deep learning. Lois: Himanshu Raj, our Principal AI/ML Instructor, joins us for today's episode. Hi Himanshu! Let's dive right in. What is machine learning?  01:12 Himanshu: Machine learning lets computers learn from examples to make decisions or predictions without being told exactly what to do. They help computers learn from past data and examples so they can spot patterns and make smart decisions just like humans do, but faster and at scale.  01:31 Nikita: Can you give us a simple analogy so we can understand this better? Himanshu: When you train a dog to sit or fetch, you don't explain the logic behind the command. Instead, you give this dog examples and reinforce correct behavior with rewards, which could be a treat, a pat, or a praise. Over time, the dog learns to associate the command with the action and reward. Machine learning learns in a similar way, but with data instead of dog treats. We feed a mathematical system called models with multiple examples of input and the desired output, and it learns the pattern. It's trial and error, learning from the experience.  Here is another example. Recognizing faces. Humans are incredibly good at this, even as babies. We don't need someone to explain every detail of the face. We just see many faces over time and learn the patterns. Machine learning models can be trained the same way. We showed them thousands or millions of face images, each labeled, and they start to detect patterns like eyes, nose, mouth, spacing, different angles. So eventually, they can recognize faces they have seen before or even match new ones that are similar. So machine learning doesn't have any rules, it's just learning from examples. This is the kind of learning behind things like face ID on your smartphone, security systems that recognizes employees, or even Facebook tagging people in your photos. 03:05 Lois: So, what you're saying is, in machine learning, instead of telling the computer exactly what to do in every situation, you feed the model with data and give it examples of inputs and the correct outputs. Over time, the model figures out patterns and relationships within the data on its own, and it can make the smart guess when it sees something new. I got it! Now let's move on to how machine learning actually works? Can you take us through the process step by step? Himanshu: Machine learning actually happens in three steps. First, we have the input, which is the training data. Think of this as showing the model a series of examples. It could be images of historical sales data or customer complaints, whatever we want the machine to learn from. Next comes the pattern finding. This is the brain of the system where the model starts spotting relationships in the data. It figures out things like customer who churn or leave usually contacts support twice in the same month. It's not given rules, it just learns patterns based on the example. And finally, we have output, which is the prediction or decision. This is the result of all this learning. Once trained, the computer or model can say this customer is likely to churn or leave. It's like having a smart assistant that makes fast, data-driven guesses without needing step by step instruction. 04:36 Nikita: What are the main elements in machine learning? Himanshu: In machine learning, we work with two main elements, features and labels. You can think of features as the clues we provide to the model, pieces of information like age, income, or product type. And the label is the solution we want the model to predict, like whether a customer will buy or not.  04:55 Nikita: Ok, I think we need an example here. Let's go with the one you mentioned earlier about customers who churn. Himanshu: Imagine we have a table with data like customer age, number of visits, whether they churned or not. And each of these rows is one example. The features are age and visit count. The label is whether the customer churned, that is yes or no. Over the time, the model might learn patterns like customer under 30 who visit only once are more likely to leave. Or frequent visitors above age 45 rarely churn. If features are the clues, then the label is the solution, and the model is the brain of the system. It's what's the machine learning builds after learning from many examples, just like we do. And again, the better the features are, the better the learning. ML is just looking for patterns in the data we give it. 05:51 Lois: Ok, we're with you so far. Let's talk about the different types of machine learning. What is supervised learning? Himanshu: Supervised learning is a type of machine learning where the model learns from the input data and the correct answers. Once trained, the model can use what it learned to predict the correct answer for new, unseen inputs. Think of it like a student learning from a teacher. The teacher shows labeled examples like an apple and says, "this is an apple." The student receives feedback whether their guess was right or wrong. Over time, the student learns to recognize new apples on their own. And that's exactly how supervised learning works. It's learning from feedback using labeled data and then make predictions. 06:38 Nikita: Ok, so supervised learning means we train the model using labeled data. We already know the right answers, and we're essentially teaching the model to connect the dots between the inputs and the expected outputs. Now, can you give us a few real-world examples of supervised learning? Himanshu: First, house price prediction. In this case, we give the model features like a square footage, location, and number of bedrooms, and the label is the actual house price. Over time, it learns how to predict prices for new homes. The second one is email: spam or not. In this case, features might include words in the subject line, sender, or links in the email. The label is whether the email is spam or not. The model learns patterns to help us filter our inbox, as you would have seen in your Gmail inboxes. The third one is cat versus dog classification. Here, the features are the pixels in an image, and the label tells us whether it's a cat or a dog. After seeing many examples, the model learns to tell the difference on its own. Let's now focus on one very common form of supervised learning, that is regression. Regression is used when we want to predict a numerical value, not a category. In simple terms, it helps answer questions like, how much will it be? Or what will be the value be? For example, predicting the price of a house based on its size, location, and number of rooms. Or estimating next quarter's revenue based on marketing spend.  08:18 Lois: Are there any other types of supervised learning? Himanshu: While regression is about predicting a number, classification is about predicting a category or type. You can think of it as the model answering is this yes or no, or which group does this belong to.  Classification is used when the goal is to predict a category or a class. Here, the model learns patterns from historical data where both the input variables, known as features, and the correct categories, called labels, are already known.  08:53 Ready to level-up your cloud skills? The 2025 Oracle Fusion Cloud Applications Certifications are here! These industry-recognized credentials validate your expertise in the latest Oracle Fusion Cloud solutions, giving you a competitive edge and helping drive real project success and customer satisfaction. Explore the certification paths, prepare with MyLearn, and position yourself for the future. Visit mylearn.oracle.com to get started today. 09:25 Nikita: Welcome back! So that was supervised machine learning. What about unsupervised machine learning, Himanshu? Himanshu: Unlike supervised learning, here, the model is not given any labels or correct answers. It just handed the raw input data and left to make sense of it on its own.  The model explores the data and discovers hidden patterns, groupings, or structures on its own, without being explicitly told what to look for. And it's more like a student learning from observations and making their own inferences. 09:55 Lois: Where is unsupervised machine learning used? Can you take us through some of the use cases? Himanshu: The first one is product recommendation. Customers are grouped based on shared behavior even without knowing their intent. This helps show what the other users like you also prefer. Second one is anomaly detection. Unusual patterns, such as fraud, network breaches, or manufacturing defects, can stand out, all without needing thousands of labeled examples. And third one is customer segmentation. Customers can be grouped by purchase history or behavior to tailor experiences, pricing, or marketing campaigns. 10:32 Lois: And finally, we come to deep learning. What is deep learning, Himanshu? Himanshu: Humans learn from experience by seeing patterns repeatedly. Brain learns to recognize an image by seeing it many times. The human brain contains billions of neurons. Each neuron is connected to others through synapses. Neurons communicate by passing signals. The brain adjusts connections based on repeated stimuli. Deep learning was inspired by how the brain works using artificial neurons and connections. Just like our brains need a lot of examples to learn, so do the deep learning models. The more the layers and connections are, the more complex patterns it can learn. The brain is not hard-coded. It learns from patterns. Deep learning follows the same idea. Metaphorically speaking, a deep learning model can have over a billion neurons, more than a cat's brain, which have around 250 million neurons. Here, the neurons are mathematical units, often called nodes, or simply as units. Layers of these units are connected, mimicking how biological neurons interact. So deep learning is a type of machine learning where the computer learns to understand complex patterns. What makes it special is that it uses neural networks with many layers, which is why we call it deep learning. 11:56 Lois: And how does deep learning work? Himanshu: Deep learning is all about finding high-level meaning from low-level data layer by layer, much like how our brains process what we see and hear. A neural network is a system of connected artificial neurons, or nodes, that work together to learn patterns and make decisions.  12:15 Nikita: I know there are different types of neural networks, with ANNs or Artificial Neural Networks being the one for general learning. How is it structured? Himanshu: There is an input layer, which is the raw data, which could be an image, sentence, numbers, a hidden layer where the patterns are detected or the features are learned, and the output layer where the final decision is made. For example, given an image, is this a dog? A neural network is like a team of virtual decision makers, called artificial neurons, or nodes, working together, which takes input data, like a photo, and passes it through layers of neurons. And each neuron makes a small judgment and passes its result to the next layer.  This process happens across multiple layers, learning more and more complex patterns as it goes, and the final layer gives the output. Imagine a factory assembly line where each station, or the layer, refines the input a bit more. By the end, you have turned raw parts into something meaningful. And this is a very simple analogy. This structure forms the foundations of many deep learning models.  More advanced architectures, like convolutional neural networks, CNNs, for images, or recurrent neural networks, RNN, for sequences built upon this basic idea. So, what I meant is that the ANN is the base structure, like LEGO bricks. CNNs and RNNs use those same bricks, but arrange them in a way that are better suited for images, videos, or sequences like text or speech.  13:52 Nikita: So, why do we call it deep learning? Himanshu: The word deep in deep learning does not refer to how profound or intelligent the model is. It actually refers to the number of layers in the neural network. It starts with an input layer, followed by hidden layers, and ends with an output layer. The layers are called hidden, in the sense that these are black boxes and their data is not visible or directly interpretable to the user. Models which has only one hidden layer is called shallow learning. As data moves, each layer builds on what the previous layer has learned. So layer one might detect a very basic feature, like edges or colors in an image. Layer two can take those edges and starts forming shapes, like curves or lines. And layer three use those shapes to identify complete objects, like a face, a car, or a person. This hierarchical learning is what makes deep learning so powerful. It allows the model to learn abstract patterns and generalize across complex data, whether it's visual, audio, or even language. And that's the essence of deep learning. It's not just about layers. It's about how each layer refines the information and one step closer to understanding. 15:12 Nikita: Himanshu, where does deep learning show up in our everyday lives? Himanshu: Deep learning is not just about futuristic robots, it's already powering the tools we use today. So think of when you interact with a virtual assistant on a website. Whether you are booking a hotel, resolving a banking issue, or asking customer support questions, behind the scenes, deep learning models understand your text, interpret your intent, and respond intelligently. There are many real-life examples, for example, ChatGPT, Google's Gemini, any airline website's chatbots, bank's virtual agent. The next one is speech-to-text systems. Example, if you have ever used voice typing on your phone, dictated a message to Siri, or used Zoom's live captions, you have seen this in action already. The system listens to your voice and instantly converts it into a text. And this saves time, enhances accessibility, and helps automate tasks, like meeting transcriptions. Again, you would have seen real-life examples, such as Siri, Google Assistant, autocaptioning on Zoom, or YouTube Live subtitles. And lastly, image recognition. For example, hospitals today use AI to detect early signs of cancer in x-rays and CT scans that might be missed by the human eye. Deep learning models can analyze visual patterns, like a suspicious spot on a lung's X-ray, and flag abnormalities faster and more consistently than humans. Self-driving cars recognize stop signs, pedestrians, and other vehicles using the same technology. So, for example, cancer detection in medical imaging, Tesla's self-driving navigation, security system synchronizes face are very prominent examples of image recognition. 17:01 Lois: Deep learning is one of the most powerful tools we have today to solve complex problems. But like any tool, I'm sure it has its own set of pros and cons. What are its advantages, Himanshu? Himanshu: It is high accuracy. When trained with enough data, deep learning models can outperform humans. For example, again, spotting early signs of cancer in X-rays with higher accuracy. Second is handling of unstructured data. Deep learning shines when working with messy real-world data, like images, text, and voice. And it's why your phone can recognize your face or transcribe your speech into text. The third one is automatic pattern learning. Unlike traditional models that need hand-coded features, deep learning models figure out important patterns by themselves, making them extremely flexible. And the fourth one is scalability. Once trained, deep learning systems can scale easily, serving millions of customers, like Netflix recommending movies personalized to each one of us. 18:03 Lois: And what about its challenges? Himanshu: The first one is data and resource intensive. So deep learning demands huge amount of labeled data and powerful computing hardware, which means high cost, especially during training. The second thing is lacks explainability. These models often act like a black box. We know the output, but it's hard to explain exactly how the model reached that decision. This becomes a problem in areas like health care and finance where transparency is critical. The third challenge is vulnerability to bias. If the data contains biases, like favoring certain groups, the model will learn and amplify those biases unless we manage them carefully. The fourth and last challenge is it's harder to debug and maintain. Unlike a traditional software program, it's tough to manually correct a deep learning model if it starts behaving unpredictably. It requires retraining with new data. So deep learning offers powerful opportunities to solve complex problems using data, but it also brings challenges that require careful strategy, resources, and responsible use. 19:13 Nikita: We're taking away a lot from this conversation. Thank you so much for your insights, Himanshu.  Lois: If you're interested to learn more, make sure you log into mylearn.oracle.com and look for the AI for You course. Join us next week for part 2 of the discussion on AI Concepts & Terminology, where we'll focus on Data Science. Until then, this is Lois Houston… Nikita: And Nikita Abraham signing off! 19:39 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

President Donald Trump has announced his plan to address crime in the nation's capital. A judge has ruled on the unsealing of evidence related to Jeffrey Epstein's accomplice Ghislaine Maxwell. US officials are racing to finalize details on a meeting between Trump and Russia's leader. Trump is considering a change to federal marijuana policy. Plus, AOL is hanging up on dial-up connection. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Gm! This week Miles Jennings and Eddy Lazzarin join Yano to dive into how a16z is approaching the current crypto landscape with a big focus on token design, designation, and how Founders can think pragmatically both along legal and engineering lines to execute their project.  -- Start your day with crypto news, analysis and data from Katherine Ross. Subscribe to the Empire newsletter: https://blockworks.co/newsletter/empire?utm_source=podcasts -- Follow Eddy: https://x.com/eddylazzarinFollow Miles: https://x.com/milesjenningsFollow Jason: https://x.com/JasonYanowitzFollow Empire: https://twitter.com/theempirepod -- Join the Empire Telegram: https://t.me/+CaCYvTOB4Eg1OWJh -- SKALE is the next evolution in Layer 1 blockchains with a gas-free invisible user experience, instant finality, high speed, and robust security. SKALE is built different as it allows for limitless scalability and has already saved its 50 Million users over $11 Billion in gas fees.  SKALE is high-performance and cost-effective, making it ideal for compute-intensive applications like AI, gaming, and consumer-facing dApps. Learn more at https://skale.space and stay up to date with the gas-free invisible blockchain on X at @skalenetwork -- Katana is a DeFi-first chain built for deep liquidity and high yield. No empty emissions, just real yield and sequencer fees routed back to DeFi users.  Pre-deposit now: Earn high APRs with Turtle Club [https://app.turtle.club/campaigns/katana] or spin the wheel with Katana Krates [https://app.katana.network/krates] -- Ledn is the leading platform for Bitcoin-backed loans, offering a secure and transparent way to unlock liquidity without selling your Bitcoin. Ledn has issued over $9 billion in loans since 2018 and has never lost a single satoshi of client assets, earning a reputation as the name you can trust in the crypto space. Visit https://www.ledn.io to learn more. – Timestamps: (03:36) Progress In DC (05:42) Paul Atkins' Onchain Declaration (13:55) End of Foundation Era In Crypto (27:31) Ads (Skale, Katana) (29:02) DUNAs & BORGs (40:12) Defining Tokens (54:54) Ads (Skale, Katana) (56:25) Token Transparency & Classifications (01:07:29) When Fees? (01:15:55) Ads (LEDN) (01:16:57) Balancing PortCos (01:19:44) Common Founder FAQs — Disclaimer: Nothing said on Empire is a recommendation to buy or sell securities or tokens. This podcast is for informational purposes only, and any views expressed by anyone on the show are solely our opinions, not financial advice. Santiago, Jason, and our guests may hold positions in the companies, funds, or projects discussed.

Send us a textYou might be using AI models in pathology without even knowing if they're giving you reliable results. Let that sink in for a second—because today, we're fixing that.In this episode, I walk you through the real statistics that power—and sometimes fail—AI in digital pathology. It's episode 4 of our AI series, and we're demystifying the metrics behind both generative and non-generative AI. Why does this matter? Because accuracy isn't enough. And not every model metric tells you the whole story.If you've ever been impressed by a model's "99% accuracy," you need to hear why that might actually be a red flag. I share personal stories (yes, including my early days in Germany when I didn't even know what a "training set" was), and we break down confusing metrics like perplexity, SSIM, FID, and BLEU scores—so you can truly understand what your models are doing and how to evaluate them correctly.Together, we'll uncover how model evaluation works for:Predictive Analytics (non-generative AI)Generative AI (text/image generating models)Regression vs. Classification use casesWhy confusion matrix metrics like sensitivity and specificity still matter—and when they don't.Whether you're a pathologist, a scientist, or someone leading a digital transformation team—you need this knowledge to avoid misleading data, flawed models, and missed opportunities.

Greg Belfrage talks to listeners about weather we should change the classification of marijuana on a federal level. Lots of great responses from the listenres. See omnystudio.com/listener for privacy information.

On this episode, host Paul W. Grimm speaks with Professor Maura R. Grossman about the fundamentals of artificial intelligence and its growing influence on the legal system. They explore what AI is (and isn't), how machine learning and natural language processing work, and the differences between traditional automation and modern generative AI. In layman's terms, they discuss other key concepts, such as supervised and unsupervised learning, reinforcement training, and deepfakes, and other advances that have accelerated AI's development. Finally, they address a few potential risks of generative AI, including hallucinations, bias, and misuse in court, which sets the stage for a deeper conversation about legal implications on the next episode, "To Trust or Not to Trust: AI in Legal Practice." ABOUT THE HOSTJudge Paul W. Grimm (ret.) is the David F. Levi Professor of the Practice of Law and Director of the Bolch Judicial Institute at Duke Law School. From December 2012 until his retirement in December 2022, he served as a district judge of the United States District Court for the District of Maryland, with chambers in Greenbelt, Maryland. Click here to read his full bio.

Fruit or Vegetable. Well, it all depends on whether or not you think the vegetable is (1) a part of the plant (2) is the plant.Sources:https://en.wikipedia.org/wiki/Berryhttps://www.dictionary.com/e/fruit-vs-vegetable/https://www.ice.edu/blog/the-difference-between-fruits-and-vegetables

(00:01) Preparing for a Golf Scramble This chapter takes us to the cool climes of Flagstaff, Arizona, where top 100 teacher Jeff Smith is enjoying teaching golf at 7,000 feet. We discuss the surprising weather contrasts in Arizona and how Flagstaff provides a refuge from the blistering heat found in places like Phoenix. I share my excitement about participating in an upcoming golf scramble in Huntsville, Alabama, organized to support a high school football team and hosted by former Pittsburgh Steelers wide receiver Sammy Coates. The event is set to take place at the Redstone Arsenal military golf course, known for its impeccable condition. Reflecting on past interviews with athletes like Phil Simms, I contemplate my role in the scramble, suspecting I might serve as the comedic relief among former professional athletes. Jeff offers practical advice for preparation, emphasizing the importance of stretching to ensure a fluid swing during the game. (11:09) Navigating a Golf Scramble Challenge This chapter takes you through a lively discussion about golf techniques, specifically focusing on green reading and playing in scrambles. I share advice on improving short game and putting, particularly emphasizing the importance of feeling the slope of the green rather than relying solely on visual cues. As I prepare to play in a golf scramble in Huntsville, Alabama, I explore how athletes transitioning to golf often face unique challenges, despite their physical prowess. Through a humorous recounting of experiences with former professional athletes like Howard Kendrick, we highlight the common misconception that athleticism directly translates to golfing skill. The conversation emphasizes the importance of understanding club face angles and precision, as even slight misalignments can lead to significant directional errors in powerful swings. (27:59) Classification of Game, Sport, Athletic Endeavor This chapter explores the intriguing question of what distinguishes an athletic endeavor from a sport, using golf as a focal point. We ponder whether the lack of direct defense in golf, compared to traditional sports, influences its classification as a sport or a game. The conversation touches on the role of Mother Nature as a formidable opponent in golf, with weather and terrain acting as natural defenses. We also discuss how drinking beer while playing could affect perceptions of what constitutes a sport, considering activities like bowling and golf. The episode reflects on memorable moments from the Open Championship, including a remarkable shot from the rough and the unique challenges posed by nature, and muses on whether these elements make golf a sport or simply a challenging game.

The sermon addresses the pervasive influence of worldly values and the urgent need for Christians to resist temptation and maintain spiritual purity. Drawing from 1 John 2, the message emphasizes the importance of overcoming wickedness through faith, daily engagement with God's Word, and resolute adherence to biblical standards in dress, entertainment, and conduct. It challenges listeners to examine their own lives, encouraging them to actively resist worldly pressures and embrace a life of spiritual maturity, ultimately defining true faith as a transformative force that empowers believers to overcome the challenges of the world and live victoriously for Christ.

The Queen stage of the Tour de France is now behind us. One mountain stage remains. Ben O'Connor played perfect legs with perfect tactics and took home a huge win for Jayco, while behind the battle for yellow wimpered and the battle for third, the white jersey, and the crucial Red Bull classification were absolutely firing. 

The sermon emphasizes the vital importance of consistent engagement with Scripture and prayer for spiritual growth, warning against complacency that leads to a preference for man-centered teachings and a stunted faith. It distinguishes between different generations within the church—children, fathers, and young men—highlighting the need for children to overcome the world, fathers to share intimate, experienced knowledge of God through prayer, and young men to actively serve and witness. Ultimately, the message underscores the necessity of enduring trials and remaining faithful to God's Word, particularly for senior saints, who serve as crucial prayer warriors and spiritual backbones of the church, while encouraging all believers to actively cultivate a deep and abiding relationship with Christ.

Another wonderful week in the communal Schauer, this time we're discussing how to develop trust with others! Please keep in mind that this is the structure for trust (the frame) and that your interpretation and expression of trust (the art) are complimentary, not negating one another. I will be doing a part three to cover some accessibility nuances since we did discuss “work value.” Hopefully you finish this episode feeling refreshed and able to build trust in your own life.  You should also rate this 5 stars! (Please) Resources How to Get Into Research as a Hobby  https://open.substack.com/pub/sarahschauer/p/how-to-start-to-researching-as-a?r=3yb1cq&utm_campaign=post&utm_medium=web  Subscribe to my Substack :) Neural Correlates of Trust  https://www.pnas.org/doi/10.1073/pnas.0710103104 Revisiting the Morphology and Classification of the Paracingulate Gyrus with Commentaries on Ambiguous Cases  https://pmc.ncbi.nlm.nih.gov/articles/PMC8301833/ Affective Regulation https://www.drmitchkeil.com/glossary/affect-regulation/#:~:text=Affect%20Regulation%20refers%20to%20the,for%20addressing%20trauma%20and%20distress. Trust Me: Social Games are Better Than Social Icebreakers in Building Trust  https://www.researchgate.net/publication/309024508_Trust_Me_Social_Games_are_Better_than_Social_Icebreakers_at_Building_Trust Definition of Contingent Trust https://lsd.law/define/contingent-trust Create Your Own Personal Constitution: A Blueprint for Living Your Best Life https://medium.com/storyangles/create-your-personal-constitution-a-blueprint-for-living-your-best-life-fb6fc2da39fe# Why Personal Boundaries Are Important and How to Develop Them https://fearlessliving.org/why-personal-boundaries-are-important-and-how-to-develop-them/ Levels of Measurement https://byjus.com/maths/scales-of-measurement/ Faith, Truth, and Forgiveness: How Your Brain Processes Abstract Thought: https://www.cmu.edu/news/stories/archives/2019/october/abstract-thought.html On the Art and Craft of Doing Science - Kenneth Catania Research Is Ceremony: Indigenous Research Methods - Shawn Wilson Your Brain on Art - Susan Magsamen & Ivy Ross Chapter 5: Amplifying Learning - for my breakdown of saliency & it's importance when learning Thank you for checking the resources, it means a lot to me. Have a great week. Get this new customer offer and your 3-month Unlimited wireless plan for just 15 bucks a month at https://MINTMOBILE.com/SCHAUER Learn more about your ad choices. Visit podcastchoices.com/adchoices

Quick PSA! Red Meat Does NOT Cause Cancer! Hard Facts About the WHO IARC Cancer Classifications on Meat and Cancer.   If you liked this and want to learn more go to my new website www.DrAnthonyChaffee.com   ✅Join my PATREON for early releases, bonus content, and weekly Zoom meetings! https://www.patreon.com/AnthonyChaffeeMD ✅Sign up for our 30-day carnivore challenge and group here! https://www.howtocarnivore.com/ ✅Stockman Steaks, Australia Discount link for home delivered frozen grass-fed and grass finished pasture raised meat locally sourced here in Australia! Use discount code "CHAFFEE" for free gift with qualifying orders! http://www.stockmansteaks.com.au/chaffee ✅ 60-minute consultation with Dr Chaffee https://calendly.com/anthonychaffeemd/60-minute-consultation   Sponsors and Affiliates: ✅ Brand Ambassador for Stone and Spear tallow and soaps referral link https://www.stoneandspeartallow.com/?ref=gx0gql8b Discount Code "CHAFFEE" for 10% off ✅ Carnivore t-shirts from the Plant Free MD  www.plantfreetees.com ✅THE CARNIVORE BAR: Discount Code "Anthony" for 10% off all orders!   https://the-carnivore-bar.myshopify.com/?sca_ref=1743809.v3IrTuyDIi ✅Schwank Grill (Natural Gas or Propane) https://glnk.io/503n/anthonychaffeemd $150 OFF with Discount Code: ANTHONYMD ✅X3 bar system with discount code "DRCHAFFEE" https://www.kqzyfj.com/click-100676052-13511487 ✅Shop Amazon https://www.amazon.com/shop/anthonychaffeemd?ref=ac_inf_hm_vp   And please like and subscribe to my podcast here and Apple/Google podcasts, as well as my YouTube Channel to get updates on all new content, and please consider giving a 5-star rating as it really helps!   This podcast is for general informational purposes only and does not constitute the practice of medicine, nursing or other professional health care services, including the giving of medical advice, and no doctor/patient relationship is formed. The use of information on this podcast or materials linked from this podcast is at the user's own risk. The content of this podcast is not intended to be a substitute for professional medical advice, diagnosis, or treatment. Users should not disregard or delay in obtaining medical advice for any medical condition they may have and should seek the assistance of their health care professionals for any such conditions.

The sermon explores the concept of spiritual classification within the Christian life, drawing parallels to military classifications and examining three stages of growth: new believers whose sins are forgiven for Christ's name's sake, maturing Christians who have grown in knowledge but still require foundational truths, and ultimately, those who have fully matured through disciplined study and prayer. It emphasizes the transformative power of Christ's sacrifice, highlighting how God's grace removes past sins and refines believers, preparing them for eternal purposes and ultimately shaping them into valuable jewels for His kingdom, urging listeners to cultivate a daily walk with God through His Word.

The Trump administration is creating a new classification for non-career employees. President Donald Trump signed an executive order establishing Schedule G that would let agencies hire non-career employees who engage in policy-making or policy-advocating work. These employees would leave their position when the president's term is over. The EO says Schedule G will improve operations, particularly in agencies like the Department of Veterans Affairs, by streamlining appointments for key policy roles. Current authorities under Schedule C or the new Schedule Policy/Career do not provide for non-career appointments to policy-making or policy-advocating roles. The White House says this leaves a gap in federal hiring categories.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Season 5, Episode 4This week we are joined by Dr Kevin Guyan to discuss his new book Rainbow Trap: Queer Lives, Classifications, and the Dangers of Inclusion.Kevin is a writer and researcher whose work explores the intersection of data and identity. He is the author of Rainbow Trap and the brilliant Queer Data: Using Gender, Sex and Sexuality Data for Action. Kevin is a Chancellor's Fellow at the University of Edinburgh and Director of the Gender + Sexuality Data Lab.Recommendations discussed in this episode:

Episode 198: Fatigue.  Future doctors Redden and Ibrahim discuss with Dr. Arreaza the different causes of fatigue, including physical and mental illnesses. Dr. Arreaza describes the steps to evaluate fatigue. Some common misconceptions are explained, such as vitamin D deficiency and “chronic Lyme disease”. Written by Michael Ibrahim, MSIV, and Jordan Redden, MSIV, Ross University School of Medicine. Edits and comments by Hector Arreaza, MDYou are listening to Rio Bravo qWeek Podcast, your weekly dose of knowledge brought to you by the Rio Bravo Family Medicine Residency Program from Bakersfield, California, a UCLA-affiliated program sponsored by Clinica Sierra Vista, Let Us Be Your Healthcare Home. This podcast was created for educational purposes only. Visit your primary care provider for additional medical advice.Dr. Arreaza: Today is a great day to talk about fatigue. It is one of the most common and most complex complaints we see in primary care. It involves physical, mental, and emotional health. So today, we're walking through a case, breaking down causes, red flags, and how to work it up without ordering the entire lab catalog.Michael:Case: This is a 34-year-old female who comes in saying, "I've been feeling drained for the past 3 months." She says she's been sleeping 8 hours a night but still wakes up tired. No recent illnesses, no weight loss, fever, or night sweats. She denies depression or anxiety but does report a lot of work stress and taking care of her two little ones at home. She drinks 2 cups of coffee a day, doesn't drink alcohol, and doesn't use drugs. No medications, just a multivitamin. Regular menstrual cycles—but she's noticed they've been heavier recently.Jordan:Fatigue is a persistent sense of exhaustion that isn't relieved by rest. It's different from sleepiness or muscle weakness.Classification based on timeline:    •   Acute fatigue: less than 1 month    •   Subacute: 1 to 6 months    •   Chronic: more than 6 monthsThis patient's case is subacute—going on 3 months now.Dr. Arreaza:And we can think about fatigue in types:    •   Physical fatigue: like muscle tiredness after activity    •   Mental fatigue: trouble concentrating or thinking clearly (physical + mental when you are a medical student or resident)    •    Pathological fatigue: which isn't proportional to effort and doesn't get better with restAnd of course, there's chronic fatigue syndrome, also called myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), which is a diagnosis of exclusion after 6 months of disabling fatigue with other symptoms.Michael:The differential is massive. So, we can also group it by systems.Jordan:Let's run through the big ones.Endocrine / Metabolic Causes    • Hypothyroidism: A classic cause of fatigue. Often associated with cold intolerance, weight gain, dry skin, and constipation. May be subtle and underdiagnosed, especially in women.    • Diabetes Mellitus: Both hyperglycemia and hypoglycemia can cause fatigue. Look for polyuria, polydipsia, weight loss, or blurry vision in undiagnosed diabetes.    • Adrenal Insufficiency: Think of this when fatigue is paired with hypotension, weight loss, salt craving, or hyperpigmentation. Can be primary (Addison's) or secondary (e.g., due to long-term steroid use).Michael: Hematologic Causes    • Anemia (especially iron deficiency): Very common, especially in menstruating women. Look for fatigue with pallor, shortness of breath on exertion, and sometimes pica (craving non-food items).     • Vitamin B12 or Folate Deficiency: B12 deficiency may present with fatigue plus neurologic symptoms like numbness, tingling, or gait issues. Folate deficiency tends to present with megaloblastic anemia and fatigue.    • Anemia of Chronic Disease: Seen in patients with chronic inflammatory conditions like RA, infections, or CKD. Typically mild, normocytic, and improves when the underlying disease is treated.Michael: Psychiatric Causes    • Depression: A major driver of fatigue, often underreported. May include anhedonia, sleep disturbance, appetite changes, or guilt. Sometimes presents with only somatic complaints.    • Anxiety Disorders: Mental fatigue, poor sleep quality, and hypervigilance can leave patients feeling constantly drained.    • Burnout Syndrome: Especially common in caregivers, healthcare workers, and educators. Emotional exhaustion, depersonalization, and reduced personal accomplishment are key features.Jordan: Infectious Causes    • Epstein-Barr Virus (EBV):Mononucleosis is a well-known cause of fatigue, sometimes lasting weeks. May also have sore throat, lymphadenopathy, and splenomegaly.    • HIV:Consider it in high-risk individuals. Fatigue can be an early sign, along with weight loss, recurrent infections, or night sweats.    • Hepatitis (B or C):Can present with chronic fatigue, especially if liver enzymes are elevated. Screen at-risk individuals.    • Post-viral Syndromes / Long COVID:Fatigue that lingers for weeks or months after viral infection. Often, it includes brain fog, muscle aches, and post-exertional malaise.Important: Chronic Lyme disease is a controversial term without a consistent clinical definition and is often used to describe patients with persistent, nonspecific symptoms not supported by objective evidence of Lyme infection. Leading medical organizations reject the term and instead recognize "post-treatment Lyme disease syndrome" (PTLDS) for persistent symptoms following confirmed, treated Lyme disease, emphasizing that prolonged antibiotic therapy is not effective. Research shows no benefit—and potential harm—from extended antibiotic use, and patients with unexplained chronic symptoms should be thoroughly evaluated for other possible diagnoses.Michael: Cardiopulmonary Causes    •   Congestive Heart Failure (CHF): Fatigue from poor perfusion and low cardiac output. Often comes with dyspnea on exertion, edema, and orthopnea.    •   Chronic Obstructive Pulmonary Disease (COPD): Look for a smoking history, chronic cough, and fatigue from hypoxia or the work of breathing.    •   Obstructive Sleep Apnea (OSA): Daytime fatigue despite adequate hours of sleep. Patients may snore, gasp, or report morning headaches. High suspicion in obese or hypertensive patients.Jordan:Autoimmune / Inflammatory Causes    •   Systemic Lupus Erythematosus (SLE): Fatigue is often an early symptom. May also see rash, arthritis, photosensitivity, or renal involvement.    •   Rheumatoid Arthritis (RA): Fatigue from systemic inflammation. Morning stiffness, joint pain, and elevated inflammatory markers point to RA.    •   Fibromyalgia: A chronic pain syndrome with widespread tenderness, fatigue, nonrestorative sleep, and sometimes cognitive complaints ("fibro fog").Cancer / Malignancy    •   Leukemia, lymphoma, or solid tumors: Fatigue can be the first symptom, often accompanied by weight loss, night sweats, or unexplained fevers. Consider when no other cause is evident.Michael:Medications:Common culprits include:    ◦   Beta-blockers: Can slow heart rate too much.    ◦   Antihistamines: Sedating H1 blockers like diphenhydramine.    ◦   Sedatives or sleep aids: Can cause grogginess and daytime sedation.    •   Substance Withdrawal: Fatigue can be seen in withdrawal from alcohol, opioids, or stimulants. Caffeine withdrawal, though mild, can also contribute.Dr. Arreaza:Whenever we evaluate fatigue, we need to keep an eye out for red flags. These should raise suspicion for something more serious:    •   Unintentional weight loss    •   Night sweats    •   Persistent fever    •   Neurologic symptoms    •   Lymphadenopathy    •   Jaundice    •   Palpitations or chest painThis patient doesn't have these—but that doesn't mean we stop here.Dr. Arreaza:Those are a lot of causes, we can evaluate fatigue following 7 steps:Characterize the fatigue.Look for organic illness.Evaluate medications and substances.Perform psychiatric screening.Ask questions about quantity and quality of sleep.Physical examination.Undertake investigations.So, students, do we send the whole lab panel?Michael:Not necessarily. Labs should be guided by history and physical. But here's a good initial panel:    •   CBC: To check for anemia or infection    • TSH: Screen for hypothyroidism    • CMP: Look at electrolytes, kidney, and liver function    • Ferritin and iron studies    • B12, folate    • ESR/CRP for inflammation (not specific)    • HbA1c if diabetes is on the radarJordan:And if needed, consider:    • HIV, EBV, hepatitis panel    • ANA, RF    • Cortisol or ACTH stimulation testImaging? Now that's rare—unless there are specific signs. Like chest X-ray for possible cancer or TB, or sleep study if you suspect OSA.Dr. Arreaza:Unaddressed fatigue isn't just inconvenient. It can impact on quality of life, affect job performance, lead to mood disorders, delay diagnosis of serious illness, increase risk of accidents—especially driving. So, don't ignore your patients with fatigue!Jordan:And some people—like women, caregivers, or shift workers—are especially at risk.Michael:The cornerstone of treatment is addressing the underlying cause.Jordan:If it's iron-deficiency anemia—treat it. If it's depression—get mental health involved. But there's also: Lifestyle Support: Better sleep hygiene, light physical activity, mindfulness or CBT for stress, balanced nutrition—especially iron and protein, limit caffeine and alcoholDr. Arreaza:Sometimes medications help—but rarely. And for chronic fatigue syndrome, the current best strategies are graded exercise therapy and CBT, along with managing specific symptoms. Beta-alanine has potential to modestly improve muscular endurance and reduce fatigue in older adults, but more high-quality research is needed.SSRI: fluoxetine and sertraline. Iron supplements: Even without anemia, but low ferritin [Anecdote about low ferritin patient]Jordan:This case reminds us to take fatigue seriously. In her case, it may be multifactorial—work stress, caregiving burden, and possibly iron-deficiency anemia. So, how would we wrap up this conversation, Michael?Michael:We don't need to order everything under the sun. A focused history and exam, targeted labs, and being alert to red flags can guide us.Jordan:And don't forget the basics—sleep, stress, and nutrition. These are just as powerful as any prescription.Dr. Arreaza:We hope today's episode on fatigue has given you a clear framework and some practical tips. If you enjoyed this episode, share it and subscribe for more evidence-based medicine!Jordan:Take care—and get some rest~___________________________Even without trying, every night you go to bed a little wiser. Thanks for listening to Rio Bravo qWeek Podcast. We want to hear from you, send us an email at RioBravoqWeek@clinicasierravista.org, or visit our website riobravofmrp.org/qweek. See you next week! _____________________References:DynaMed. (2023). Fatigue in adults. EBSCO Information Services. https://www.dynamed.com (Access requires subscription)Jason, L. A., Sunnquist, M., Brown, A., Newton, J. L., Strand, E. B., & Vernon, S. D. (2015). Chronic fatigue syndrome versus systemic exertion intolerance disease. Fatigue: Biomedicine, Health & Behavior, 3(3), 127–141. https://doi.org/10.1080/21641846.2015.1051291Kroenke, K., & Mangelsdorff, A. D. (1989). Common symptoms in ambulatory care: Incidence, evaluation, therapy, and outcome. The American Journal of Medicine, 86(3), 262–266. https://doi.org/10.1016/0002-9343(89)90293-3National Institute for Health and Care Excellence. (2021). Myalgic encephalomyelitis (or encephalopathy)/chronic fatigue syndrome: Diagnosis and management (NICE Guideline No. NG206). https://www.nice.org.uk/guidance/ng206UpToDate. (n.d.). Approach to the adult patient with fatigue. Wolters Kluwer. https://www.uptodate.com (Access requires subscription)Theme song, Works All The Time by Dominik Schwarzer, YouTube ID: CUBDNERZU8HXUHBS, purchased from https://www.premiumbeat.com/.

Judicial scrutiny, vital for U.S. constitutional law, assesses if laws comply with the Fourteenth Amendment's Equal Protection and Due Process Clauses. It has three levels: Rational Basis Review (lenient, for non-fundamental rights), Intermediate Scrutiny (mid-tier, for quasi-suspect classifications like gender), and Strict Scrutiny (highest, for fundamental rights or suspect classifications like race, often "fatal in fact").The Equal Protection Clause, requiring similar treatment for similarly situated people, has evolved, notably expanding to corporations. However, "pluralism anxiety" has led to limitations on traditional, group-based civil rights by restricting heightened scrutiny classifications, foreclosing disparate impact claims without discriminatory intent, and curbing congressional enforcement powers under Section 5.Despite these limitations, the Court has shifted to "liberty-based dignity claims," using due process liberty analysis to protect subordinated groups, as seen in cases like Lawrence v. Texas (sodomy laws) and Roe v. Wade (abortion rights). This approach often frames rights universally, circumventing traditional scrutiny bars and Section 5 limitations.Critics argue the scrutiny framework has ambiguous boundaries, allows too much judicial discretion, is overly deferential in rational basis, and struggles with modern issues and intersectional discrimination.U.S. v. Skrmetti, addressing gender-affirming care for minors, is a pivotal case that will define the application of the Equal Protection Clause to transgender issues. Arguments revolve around whether the law discriminates on sex, age, or transgender status, and the state's justification for the ban. The outcome, expected in June 2025, will significantly impact equal protection jurisprudence.In conclusion, the scrutiny framework, while foundational, faces challenges in adapting to societal changes. The shift to liberty-based dignity claims offers a new avenue for protecting rights, but cases like Skrmetti highlight ongoing debates and the framework's future.

Sandwiched between the famed Médoc AOCs of Margaux in the south and Pauillac in the north, Saint Julien has one of the highest concentrations of classified growths from the 1855 Classification in Bordeaux. This red wine only AOC is just 910 ha/2,250acres, which is 6% of the Médoc vineyard. It is one-sixth the size of Pauillac. It makes an average of about 6 million bottles a year. Image courtesy of Château Léoville Barton  But this densely planted appellation may be small but what it lacks in size, it makes up for in quality. Saint Julien is considered the most consistent of the Médoc communes and it is known for Cabernet Sauvignon dominant wines with the perfect balance of tannin, flavor, and acidity year after year.   In this show, I cover what makes Saint Julien so unique. As in the other Greats of Bordeaux shows, I review the history, terroir, climate, and then discuss the top Châteaux.   For reference,  Here is a link to the 1855 Classification   Full show notes and all back episodes are on Patreon. Become a member today! www.patreon.com/winefornormalpeople _______________________________________________________________   Check out my exclusive sponsor, Wine Access.  They have an amazing selection -- once you get hooked on their wines, they will be your go-to! Make sure you join the Wine Access-Wine For Normal People wine club for wines I select delivered to you four times a year!    To register for an AWESOME, LIVE WFNP class with Elizabeth or get a class gift certificate for the wine lover in your life go to: www.winefornormalpeople.com/classes  

In the podcast, Swine Extension Educator Sarah Schieck Boelke speaks with Drs. Kim VanderWaal and Igor Paploski about PRRS virus Classification. Both are faculty members in the University of Minnesota Veterinary Population Medicine department. PRRS virus Classification is basically naming of the PRRS virus according to the genetic sequencing of the virus. Kim and Igor explain how PRRS viruses were classified previously and changes to how they are being classified based on their work.Learn more about PRRS virus classification featured in the podcastSwine Health Information Center fact sheet on the epidemiological insights on the PRRS-Loom Variant webtoolLink to PRRS-Loom Variant webtoolJournal article published in Epidemiology, Volume 10, Issue 2. doi: 10.1128/msphere.00709-24

Send comments and feedbackILAE's updated seizure classification position paper was published in Epilepsia in April 2025. Sharp Waves talked with Dr. Sandor Beniczky about the updates and how they will impact research and clinical care.The position paper is open access and available online. Sharp Waves episodes are meant for informational purposes only, and not as clinical or medical advice.Let us know how we're doing: podcast@ilae.org.The International League Against Epilepsy is the world's preeminent association of health professionals and scientists, working toward a world where no person's life is limited by epilepsy. Visit us on Facebook, Instagram, and LinkedIn.

Rainbow Trap: Queer Lives, Classifications and the Dangers of Inclusion (Bloomsbury, 2025) by Dr. Kevin Guyan reveals how the fight for LGBTQ equalities in the UK is shaped – and constrained – by the classifications we encounter every day. Looking across six systems – the police and the recording of hate crimes; dating apps and digital desire; outness in the film and television industry; borders and LGBTQ asylum seekers; health and fitness activities; and DEI initiatives in the workplace – Rainbow Trap documents how inclusive interventions – such as new legislation, revamped diversity policies and tech fixes – have attempted to bring historically marginalized communities out of the shadows.Yet, as part of the bargain, LGBTQ people need to locate themselves in an ever-growing list of classifications, categories and labels to 'make sense' to the very systems they are seeking to access. This requirement to be classified catches LGBTQ communities in a rainbow trap. Because when we look beyond the welcoming veneer of inclusive interventions, we uncover sorting processes that determine what LGBTQ lives are valued and what queer futures are possible. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

Rainbow Trap: Queer Lives, Classifications and the Dangers of Inclusion (Bloomsbury, 2025) by Dr. Kevin Guyan reveals how the fight for LGBTQ equalities in the UK is shaped – and constrained – by the classifications we encounter every day. Looking across six systems – the police and the recording of hate crimes; dating apps and digital desire; outness in the film and television industry; borders and LGBTQ asylum seekers; health and fitness activities; and DEI initiatives in the workplace – Rainbow Trap documents how inclusive interventions – such as new legislation, revamped diversity policies and tech fixes – have attempted to bring historically marginalized communities out of the shadows.Yet, as part of the bargain, LGBTQ people need to locate themselves in an ever-growing list of classifications, categories and labels to 'make sense' to the very systems they are seeking to access. This requirement to be classified catches LGBTQ communities in a rainbow trap. Because when we look beyond the welcoming veneer of inclusive interventions, we uncover sorting processes that determine what LGBTQ lives are valued and what queer futures are possible. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/gender-studies

Rainbow Trap: Queer Lives, Classifications and the Dangers of Inclusion (Bloomsbury, 2025) by Dr. Kevin Guyan reveals how the fight for LGBTQ equalities in the UK is shaped – and constrained – by the classifications we encounter every day. Looking across six systems – the police and the recording of hate crimes; dating apps and digital desire; outness in the film and television industry; borders and LGBTQ asylum seekers; health and fitness activities; and DEI initiatives in the workplace – Rainbow Trap documents how inclusive interventions – such as new legislation, revamped diversity policies and tech fixes – have attempted to bring historically marginalized communities out of the shadows.Yet, as part of the bargain, LGBTQ people need to locate themselves in an ever-growing list of classifications, categories and labels to 'make sense' to the very systems they are seeking to access. This requirement to be classified catches LGBTQ communities in a rainbow trap. Because when we look beyond the welcoming veneer of inclusive interventions, we uncover sorting processes that determine what LGBTQ lives are valued and what queer futures are possible. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/film

Rainbow Trap: Queer Lives, Classifications and the Dangers of Inclusion (Bloomsbury, 2025) by Dr. Kevin Guyan reveals how the fight for LGBTQ equalities in the UK is shaped – and constrained – by the classifications we encounter every day. Looking across six systems – the police and the recording of hate crimes; dating apps and digital desire; outness in the film and television industry; borders and LGBTQ asylum seekers; health and fitness activities; and DEI initiatives in the workplace – Rainbow Trap documents how inclusive interventions – such as new legislation, revamped diversity policies and tech fixes – have attempted to bring historically marginalized communities out of the shadows.Yet, as part of the bargain, LGBTQ people need to locate themselves in an ever-growing list of classifications, categories and labels to 'make sense' to the very systems they are seeking to access. This requirement to be classified catches LGBTQ communities in a rainbow trap. Because when we look beyond the welcoming veneer of inclusive interventions, we uncover sorting processes that determine what LGBTQ lives are valued and what queer futures are possible. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/critical-theory

Rainbow Trap: Queer Lives, Classifications and the Dangers of Inclusion (Bloomsbury, 2025) by Dr. Kevin Guyan reveals how the fight for LGBTQ equalities in the UK is shaped – and constrained – by the classifications we encounter every day. Looking across six systems – the police and the recording of hate crimes; dating apps and digital desire; outness in the film and television industry; borders and LGBTQ asylum seekers; health and fitness activities; and DEI initiatives in the workplace – Rainbow Trap documents how inclusive interventions – such as new legislation, revamped diversity policies and tech fixes – have attempted to bring historically marginalized communities out of the shadows.Yet, as part of the bargain, LGBTQ people need to locate themselves in an ever-growing list of classifications, categories and labels to 'make sense' to the very systems they are seeking to access. This requirement to be classified catches LGBTQ communities in a rainbow trap. Because when we look beyond the welcoming veneer of inclusive interventions, we uncover sorting processes that determine what LGBTQ lives are valued and what queer futures are possible. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/national-security

Rainbow Trap: Queer Lives, Classifications and the Dangers of Inclusion (Bloomsbury, 2025) by Dr. Kevin Guyan reveals how the fight for LGBTQ equalities in the UK is shaped – and constrained – by the classifications we encounter every day. Looking across six systems – the police and the recording of hate crimes; dating apps and digital desire; outness in the film and television industry; borders and LGBTQ asylum seekers; health and fitness activities; and DEI initiatives in the workplace – Rainbow Trap documents how inclusive interventions – such as new legislation, revamped diversity policies and tech fixes – have attempted to bring historically marginalized communities out of the shadows.Yet, as part of the bargain, LGBTQ people need to locate themselves in an ever-growing list of classifications, categories and labels to 'make sense' to the very systems they are seeking to access. This requirement to be classified catches LGBTQ communities in a rainbow trap. Because when we look beyond the welcoming veneer of inclusive interventions, we uncover sorting processes that determine what LGBTQ lives are valued and what queer futures are possible. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/lgbtq-studies

In this case, the court considered this issue: Does a Tennessee law restricting certain medical treatments for transgender minors violate the Equal Protection Clause of the 14th Amendment?The case was decided on June 18, 2025.   The Supreme Court held that Tennessee's law prohibiting certain medical treatments for transgender minors is not subject to heightened scrutiny under the Equal Protection Clause of the Fourteenth Amendment and satisfies rational basis review. Chief Justice John Roberts authored the 6-3 majority opinion of the Court.First, the Equal Protection Clause does not require heightened scrutiny because Tennessee's law does not classify on any bases that warrant such review. The law contains only two classifications: one based on age (allowing treatments for adults but not minors) and another based on medical use (permitting puberty blockers and hormones for certain conditions but not for treating gender dysphoria). Classifications based on age or medical use receive only rational basis review—the most deferential standard of constitutional review. The law does not classify based on sex because it prohibits healthcare providers from administering these treatments to any minor for the excluded diagnoses, regardless of the minor's biological sex. When properly understood as regulating specific combinations of drugs and medical indications, the law treats all minors equally: none may receive these treatments for gender dysphoria, but minors of any sex may receive them for other qualifying conditions like precocious puberty or congenital defects.The law satisfies rational basis review because Tennessee's legislature had reasonable grounds for its restrictions. The state found that these treatments for gender dysphoria carry risks including irreversible sterility, increased disease risk, and adverse psychological consequences, while minors lack the maturity to understand these consequences and many express later regret. Tennessee also determined that the treatments are experimental with unknown long-term effects, and that gender dysphoria can often be resolved through less invasive approaches. Under rational basis review, courts must uphold laws if there are any reasonably conceivable facts supporting the classification. States have wide discretion in areas of medical and scientific uncertainty, noting that recent reports from health authorities in England and other countries have raised similar concerns about the evidence supporting these treatments for minors.Justice Clarence Thomas authored a concurring opinion, joined by Justice Amy Coney Barrett, arguing that Bostock v Clayton County (in which the Court held that Title VII of the Civil Rights Act's prohibition on discrimination because of sex includes discrimination based on transgender identity or sexual orientation) should not apply to Equal Protection Clause analysis and criticizing deference to medical experts who lack consensus and have allowed political ideology to influence their guidance on transgender treatments for minors.Justice Barrett authored a concurring opinion, joined by Justice Thomas, arguing that transgender individuals do not constitute a suspect class under the Equal Protection Clause because they lack the “obvious, immutable, or distinguishing characteristics” of a “discrete group” and because suspect class analysis should focus on a history of de jure (legal) discrimination rather than private discrimination.

“A 30% duty can turn a profitable product into a loss-maker if it's not accounted for.” — Noa Sussman, TecEx In the latest installment of the TecEx podcast series, Doug Green, Publisher of Technology Reseller News, is joined once again by Noa Sussman of TecEx to unpack the real-world financial impact of tariffs, duties, and VAT on global technology deployments—and why it's no longer just a logistics issue, but a boardroom concern. The Hidden Cost of Global Trade Decisions Sussman warns that companies still underestimate how duties and tariffs directly affect sourcing and profitability. With geopolitical tensions and shifting regulations, a 10% or 30% tariff isn't theoretical—it's immediate and impactful. “It can take a product from black to red if you don't factor it into your pricing,” Sussman explains. Sourcing from countries like China or exporting to regulated markets like Europe means companies must think beyond cost and lead times—they must assess tax exposure and long-term compliance risks. VAT: Not Just a Cost of Doing Business The discussion also dives into VAT—how it varies across jurisdictions, and how misunderstandings about reclaim rules can cause serious cash flow issues. Sussman shares how a poorly timed or misunderstood VAT payment can delay deployments and disrupt go-to-market timelines. Case Study: When Classification Goes Wrong In a striking real-world example, Sussman recounts how one client misclassified a component's country of origin, triggering a massive tariff on an entire $1 million shipment rather than a single $5,000 part. The error, based on incomplete paperwork and poor compliance oversight, led to unexpected duties, delays, and financial loss—something a strategic partner like TecEx could have prevented. Strategic Solutions: Planning and Partners From leveraging free trade agreements to properly classifying products and understanding country-specific tax rules, Sussman outlines how planning can lower costs and reduce risk. He also introduces the concept of “duty drawback programs”—a lesser-known tool that allows qualified companies to recoup paid duties under certain conditions. “Too many companies make the sale before they think about VAT or duties,” Sussman notes. “By then, it's too late.” Final Word: Don't Go It Alone For organizations deploying globally—whether into data centers or manufacturing facilities—the message is clear: bringing in a seasoned trade and compliance partner isn't optional. It's essential. To learn more about navigating global deployment and compliance with confidence, visit tecex.com.

Joining the Exchange is RCC President Dr. Randy Weber to discuss the Carnegie classification.

Featuring a slide presentation and related discussion from Dr Patrick Y Wen, including the following topics: Classification and pathologic diagnosis of gliomas (0:00) Role of IDH inhibitors in the management of low-grade gliomas (6:37) Ongoing trials and remaining questions in the management of IDH-mutant gliomas (19:53) CME information and select publications

In this Power Producers Podcast episode, David Carothers is joined by Kevin Ring, lead analyst at the Institute of Work Comp Professionals, for a deep dive into workers' compensation classifications. The discussion focuses on the challenges of ensuring that businesses are properly classified, the importance of getting class codes right, and strategies to avoid common mistakes that could lead to costly audits. With firsthand experience from multiple NCCI audits, David shares his insights on how to navigate the complexities of workers' comp class codes and protect clients from unnecessary errors. Kevin also covers the most recent changes in the industry, such as NCCI's update to code 8720 for construction estimators, and explains how businesses need to stay updated on classification changes to avoid issues down the line. This episode is packed with practical advice for agents looking to stay ahead in the workers' comp space. Key Highlights: The Importance of Classifications David and Kevin discuss why workers' compensation class codes are crucial and how an incorrect classification can result in higher premiums and audits. They emphasize the importance of understanding both the business operations and how they should be classified. Navigating NCCI Audits David shares his experience with NCCI audits, including the thorough process involved and how misunderstandings about class codes can lead to disputes. He explains why getting the right people involved upfront can prevent these issues. Changes in Workers' Comp Classifications Kevin discusses recent changes to class codes, including the addition of construction estimators to code 8720 and the merging of long-haul and short-haul trucking codes. He explains how these changes can impact businesses and how agents need to stay on top of these adjustments. Understanding Standard Exception Classifications Kevin explains standard exception classifications (like clerical, outside sales, and drivers) and how they can be applied separately from the governing class code. He also touches on the importance of understanding each code's detailed criteria to ensure accuracy. Classifying Businesses in Construction and Manufacturing Kevin dives into the complexity of classifying businesses in industries like construction and manufacturing, where different activities within the same business can require separate codes. He provides examples of how to apply the right codes depending on the work performed. The Role of Agents in Accurate Classifications David and Kevin discuss the role of insurance agents in accurately classifying businesses and ensuring that clients' operations align with the right class codes. They highlight how thorough knowledge and documentation can prevent issues during audits and save clients money. Connect with: David Carothers LinkedIn Kevin Ring LinkedIn Kyle Houck LinkedIn Visit Websites: Power Producer Base Camp Institute of WorkComp Professionals Killing Commercial Crushing Content Power Producers Podcast Policytee The Dirty 130 The Extra 2 Minutes

Did you enjoy this episode? Text us your thoughts and be sure to include the episode name.We kick off our latest accounting miniseries on lease accounting with an episode on the related presentation and disclosure requirements. We break down key considerations across the balance sheet, income statement, and statement of cash flows, including what interim and annual disclosures are required as well as the treatment of lease incentives, sublease income, and more.In this episode, we discuss:2:12 – Current versus noncurrent lease liabilities5:37 – Presentation of lease incentives11:40 – Income statement presentation of lease expense16:29 – Classification of lease payments in the statement of cash flows26:25 – Annual and interim lease disclosure requirementsFor more information, check out our Leases guide and the leasing chapter of the Financial statement presentation guide.Be sure to follow this podcast on your favorite podcast app and subscribe to our weekly newsletter to stay in the loop. About our guestsMarc Jerusalem is a PwC National Office managing director specializing in leasing. Marc consults with clients on complex lease accounting issues and is a frequent contributor to many related PwC National Office publications.Suzanne Stephani is a director in PwC's National Office specializing in the statement of cash flows, as well as the application and interpretation of the accounting guidance related to financing and leasing transactions.About our hostHeather Horn is the PwC National Office Sustainability and Thought Leader, responsible for developing our communications strategy and conveying firm positions on accounting, financial reporting, and sustainability matters. In addition, she is part of PwC's global sustainability leadership team, developing interpretive guidance and consulting with companies as they transition from voluntary to mandatory sustainability reporting. She is also the engaging host of PwC's accounting and reporting weekly podcast and quarterly webcast series.Transcripts available upon request for individuals who may need a disability-related accommodation. Please send requests to us_podcast@pwc.com

How could recording people having sex be 'for science'? Same question for sleeping with your colleagues? Well, these were just two of the practices embraced by the subject of today's conversation.Donna Drucker is returning to the podcast to discuss pioneering sexologist, Alfred Kinsey. Donna, from Columbia University, is author of The Classification of Sex.This episode was edited by Nick Thomson. The producer was Sophie Gee. The senior producer was Charlotte Long.Sign up to History Hit for hundreds of hours of original documentaries, with a new release every week and ad-free podcasts. Sign up at https://www.historyhit.com/subscribe.  You can take part in our listener survey here.All music from Epidemic Sounds.Betwixt the Sheets: History of Sex, Scandal & Society is a History Hit podcast.

Send us a textConsensus Approach for Standardization of the Timing of Brain Magnetic Resonance Imaging and Classification of Brain Injury in Neonates With Neonatal Encephalopathy/Hypoxic-Ischemic Encephalopathy: A Canadian Perspective.Mohammad K, Reddy Gurram Venkata SK, Wintermark P, Farooqui M, Beltempo M, Hicks M, Zein H, Shah PS, Garfinkle J, Sandesh S, Cizmeci MN, Fajardo C, Guillot M, de Vries LS, Pinchefsky E, Shroff M, Scott JN; Newborn Brain Health Working Group of the Canadian Neonatal Network.Pediatr Neurol. 2025 May;166:16-31. doi: 10.1016/j.pediatrneurol.2025.01.021. Epub 2025 Feb 12.PMID: 40048833 Free article.As always, feel free to send us questions, comments, or suggestions to our email: nicupodcast@gmail.com. You can also contact the show through Instagram or Twitter, @nicupodcast. Or contact Ben and Daphna directly via their Twitter profiles: @drnicu and @doctordaphnamd. The papers discussed in today's episode are listed and timestamped on the webpage linked below. Enjoy!