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[jrepin]

KDE Plasma 6.4 released

Plasma is a popular desktop (and mobile) environment for GNU/Linux and other UNIX-like operating systems. Among other things it also powers the desktop mode of Steam Deck gaming handheld. The KDE community today announced the latest release: Plasma 6.4. This fresh new release improves on nearly every front, with progress being made in accessibility, color rendering, tablet support, window management, and more.

Plasma already offered virtual desktops and customizable tiles to help organize your windows and activities, and now it lets you choose a different configuration of tiles on each virtual desktop. The Wayland session brings some new accessibility features: you can now move the pointer using your keyboard’s number pad keys, or use a three-finger touchpad pinch gesture to zoom in or out. Plasma file transfer notification now shows a speed graph, giving you a more visual idea of how fast the transfer is going, and how long it will take to complete. When any applications are in full screen mode Plasma will now enter Do Not Disturb mode and only show urgent notifications, and when you exit full screen mode, you’ll see a summary of any notifications you missed. Now when an application tries to access the microphone and finds it muted, a notification will pop up. A new feature in the Application Launcher widget will place a green New! tag next to newly installed apps, so you can easily find where something you just installed lives in the menu. The Display and Monitor page in System Settings comes with a brand new HDR calibration wizard, and support for Extended Dynamic Range (a different kind of HDR) and P010 video color format has been added. System Monitor now supports usage monitoring for AMD and Intel graphic cards, it can even show the GPU usage on a per-process basis. Spectacle, the built-in app for taking screenshots and screen recordings, has much improved design and more streamlined functionality. The background of the desktop or window now darkens when an authentication dialog shows up, helping you locate and focus on the window asking for your password. There’s a brand-new Animations page in System Settings that groups all the settings for purely visual animated effects into one place, making it easier to find and configure them. Aurorae is a newly added SVG vector graphics theme engine for KWin window decorations.

You can read more about these and many other other features in the Plasma 6.4 anounncement and complete changelog.

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[nschool]

The Role of Data Science in Healthcare and Diagnosis

Data science is changing many areas, and healthcare is one of the most important ones. Today, healthcare uses data science to help doctors find diseases early, make better decisions, and create treatments that fit each patient. Hospitals, clinics, and researchers have a lot of health data, like patient records, test results, and information from devices like fitness trackers. Data science helps to understand all this data and use it to improve health and save lives.

Why Healthcare Needs Data Science

Healthcare creates huge amounts of data every day. Each patient has a medical history, lab tests, prescriptions, and other information. But this data is often spread out and not easy to use. Data science helps by analyzing this data and finding useful patterns.

Using tools like machine learning and statistics, data scientists find important information that can help doctors and nurses make faster and better decisions. This means patients get the right care at the right time.

How Data Science Helps Healthcare

1. Finding Diseases Early

One of the biggest ways data science helps is by spotting diseases early. Doctors use data science models trained on thousands of medical images and patient data to find signs of diseases like cancer or heart problems before they become serious.

For example, some AI tools can look at breast cancer scans and find tiny changes that a doctor might miss. This helps catch cancer early when treatment is easier and more effective.

2. Predicting Health Problems

Data science can also predict which patients might get sick or need extra care. Hospitals use this to plan treatment and avoid emergencies.

For example, data models can predict if a patient might develop a serious infection like sepsis. If the model alerts the doctors early, they can start treatment sooner and save the patient’s life.

3. Making Treatment Personal

Every person is different, so one treatment might not work for everyone. Data science helps by studying a patient’s genes, lifestyle, and past treatments to suggest the best medicine or therapy for them.

In cancer treatment, for example, doctors use genetic data to choose the drugs that will work best for a patient’s specific type of cancer. This approach is called “precision medicine.”

4. Helping Doctors Read Medical Images

Reading X-rays, MRIs, or CT scans takes time and skill. Data science uses AI to help doctors by quickly analyzing these images and pointing out problems.

For example, AI can find small lung nodules on a chest X-ray, which could be early signs of lung cancer. This helps doctors make faster and more accurate diagnoses.

5. Finding New Medicines

Creating new drugs takes a long time and costs a lot of money. Data science can speed up this process by predicting which chemicals might work as medicines.

During the COVID-19 pandemic, data science helped researchers understand the virus and find possible treatments faster than ever before.

Tools Used in Healthcare Data Science

Healthcare data science uses many computer tools to do its work:

Python and R: These programming languages help analyze data and build models.

TensorFlow and PyTorch: These tools help create AI programs that learn from data.

Tableau and Power BI: These help make charts and graphs to show data clearly.

Cloud platforms like AWS and Azure: These provide places to store and process big amounts of data quickly.

Together, these tools help doctors and data scientists work as a team to improve health care.

Challenges of Using Data Science in Healthcare

Even though data science is very helpful, there are some challenges:

Privacy: Patient data is very private. It must be kept safe and only used in the right ways.

Data Quality: Sometimes data is incomplete or wrong, which can lead to mistakes.

Understanding AI: Doctors need to know how AI makes decisions to trust it, but sometimes AI is hard to understand.

Fairness: If data is biased, AI might make unfair decisions that hurt some patients.

Healthcare providers, data scientists, and regulators must work together to solve these problems carefully.

What the Future Looks Like

The future of healthcare will rely even more on data science. Some examples include:

AI assistants helping with mental health support.

Wearable devices that monitor health and alert doctors in emergencies.

Hospitals using data to manage patient care and resources better.

Digital models of patients that test treatments before trying them in real life.

As technology improves and more data becomes available, healthcare will become faster, safer, and more personal.

Conclusion

Data science is changing healthcare in many good ways. It helps find diseases early, predicts health risks, personalizes treatments, helps doctors read medical images, and speeds up drug discovery. These improvements come from using data and technology together.