标签：#Science

A recent video of a great white shark in the Mediterranean Sea offers the possibility of deriving valuable information for conservation strategies.

Cities are dynamic, not static grids, and urbanization is a "spiky," cyclical, and asynchronous process.

Five peer-reviewed papers update the design and model its expected output.

If you scrape Weibo's hot-search board you get a snapshot: ~50 trending topics, ranked, right now. That's table stakes — and on its own it's almost useless as a signal. The value isn't what is trending; it's what's moving : which topic just jumped 30 places in 20 minutes, which is decaying, which is brand-new this hour. That's velocity , and velocity is where the signal lives — for brand-crisis teams, consumer-trend desks, and anyone modelling attention in China. The catch: a single scrape can't tell you velocity. You have to diff the board against its own past, reliably, run after run. That's a stateful pipeline, and it has a few non-obvious gotchas. Here's the shape of the problem and how to handle it. Why a snapshot isn't enough Rank-right-now tells you nothing about trajectory. "#7" could be a topic on its way to #1 or one fading out of the top 50 — same row, opposite meaning. To act on a trend you need the derivative : direction, speed, and how long it's been climbing. None of that is in a single pull. The trending-delta problem Three things make "just diff the board" harder than it looks: Key by identity, not position. You can't track a topic by its rank — rank is the thing that changes. Key by the topic itself (its text/keyword) or your deltas are nonsense. State has to survive between runs. A scheduled scrape is stateless by default — each run starts cold. To compute "this rose 12 places since 30 minutes ago," you must persist the previous board and reload it next run, keyed so independent schedules don't overwrite each other. The board churns. Topics appear, peak, and fall off. You want each tagged new / rising / falling / steady / dropped , plus how long it's been on the board and its running peak — none of which exist in the raw snapshot. How to handle it (the pattern) current = pull_board () # [{topic, rank, heat}, ...] previous = load_state ( key ) # durable store that persists across runs for t in current : prev = previous . get ( t . topic ) # match o

Individual gold atoms move around to form oxidation-proof structures.

The FDA recently approved the cellular rejuvenation therapy ER-100 for human clinical trials. While vision is the first target, it could have applications for a variety of age-related disease.

Donut Lab's solid-state battery claims have been thoroughly debunked by Ryan Inis Hughes on his popular Ziroth YouTube channel. According to Hughes, Donut Lab has engaged in deliberate, calculated deception by claiming to have a solid-state battery ready for mass production. In reality, it's nothing more than a standard lithium-ion design. Hughes' investigation got an […]

The waiver "serves the public interest by promoting a second large satellite broadband constellation."

Have you ever wondered how Discord keeps your channel messages available even when a server goes down? Or how Amazon DynamoDB serves petabytes of data with single-digit millisecond latency? The unsung hero powering almost all these distributed systems is hashing — a simple but powerful technique that makes even load distribution, fast lookups, and seamless scaling possible. As more applications move to distributed cloud architectures, understanding hashing for distributed systems is no longer optional for developers. Choosing the wrong hashing algorithm can lead to cascading failures, cache stampedes, and expensive downtime. This guide breaks down every core hashing technique, real-world use cases, best practices, and common pitfalls to avoid in 2026. Table of Contents What is Hashing in Distributed Systems? Core Hashing Algorithms Explained Traditional Modulo Hashing Consistent Hashing Virtual Nodes (VNodes) Rendezvous Hashing (HRW) Jump Consistent Hash Maglev Hashing Multi-Probe Consistent Hashing Consistent Hashing with Bounded Loads Real-World Applications of Distributed Hashing Head-to-Head Algorithm Comparison Best Practices for Distributed Hashing Common Pitfalls to Avoid Conclusion References What is Hashing in Distributed Systems? Hashing in distributed systems is the practice of mapping data keys (e.g., user IDs, object keys, channel IDs) to server nodes using a deterministic hash function. The core goals are: Distribute load evenly across all nodes to avoid hotspots Enable fast lookups (O(1) or O(log N)) without a central coordinator Minimize data movement when nodes are added or removed during scaling Support fault tolerance by simplifying replication across nodes The simplest implementation is modulo-based hashing , where node_id = hash(key) % N and N is the total number of nodes. While trivial to implement, it suffers from a fatal flaw: the rehashing problem. When N changes (a node is added or removed), nearly all keys are remapped to new nodes, causin

Polystyrene can be upcycled into carbon sponge material.

Machine learning has its limits—how is it being used?

We've seen Axiom Space and Prada's collaboration on the Axiom Extravehicular Mobility Unit (AxEMU) spacesuit. Now the company has revealed the Liquid Cooling and Ventilation Garment (LCVG) that astronauts will wear underneath it when Artemis IV returns humans to the Moon in 2028. The LCVG is the all-important base layer that will keep the crew […]

How I got here On principle, you will never catch me parading myself as a some sort of expert data scientist. Technically, that's what I do in my day job, but I know I still have so much to learn because the field is broad, and to truly become expert requires dangerously ambitious levels of work ethic. I think I'm a functional data scientist who learns more as I encounter new problems daily. I'm writing this piece because in the last week or two, precisely three people have asked me questions related to transitioning into data science. As such, I thought to unify my thoughts around the topic so that I can refer anyone else who asks here--if anyone else ever asks. This article assumes you're already familiar with some of the data science entails such as data analysis, model training, prediction, etc, so I will not be doing a lecture series, just addressing some of the disconnects I have observed in conversation with people looking to transition to the field. Initial Excitement In 2026, it's easy to see what claude or chatGPT is doing and go "What sorcery is this? I must learn this trick!" and then reach out to the closest person you know who has ever mentioned anything about data or machine learning to find out how you can transition into AI. First of all, transitioning into "AI" is such a broad way to look at it. It is analogous to saying "I want to emigrate to Africa, show me how". But that's forgivable too. To cut short your initial excitement, or maybe redirect it, playing with a locally hosted LLM or making API calls to the DeepSeek endpoint is not data science, or machine learning or "AI". It's coding. And if you want to go down that route, you're better of focusing on software engineering. I say this because when you work with LLMs, the finished models to be specific, it's like using any other SaaS API out there. The difference being that you're interacting with a much less deterministic interface. But the rest of the work you do around it is pretty much a det

When I started learning Excel as part of my Data Science & Analytics course, I assumed it was just a tool for creating tables and performing basic calculations. After spending a week exploring its features, I quickly realized that Excel is much more powerful than I thought. Almost every organization generates data. Businesses track sales, schools monitor student performance, hospitals manage patient records, and marketers analyze campaign results. Before data can be analyzed, it needs to be organized, cleaned, and summarized—and that's where Excel comes in. In this article, I'll share some of the Excel concepts I've learned so far and how they're used in real-world data analysis. Understanding the Excel Workspace Before working with data, it's important to understand the basic structure of Excel. When you open Excel, you're working inside a workbook . A workbook can contain multiple worksheets (often called sheets), which help organize different sets of data. At the top of the screen is the Ribbon , which contains tabs such as Home, Insert, Page Layout, Formulas, Data, and View. The Ribbon acts like a control center where you can access Excel's tools and features. Rows run horizontally and are identified by numbers, while columns run vertically and are identified by letters. The intersection of a row and column is called a cell , where data is entered. At first, all these parts seemed overwhelming, but after using Excel regularly, navigating through them has become much easier. The Different Types of Data in Excel One of the first things I learned is that not all data is the same. Excel commonly works with: Text data (names, product categories, locations) Numeric data (sales figures, quantities, prices) Date and time data (order dates, deadlines) Logical data (TRUE or FALSE values) Understanding data types is important because Excel treats each type differently when performing calculations and analysis. Number Formats Matter More Than I Expected Another concept that

Before this week, I thought Excel was just a fancy calculator with boxes. But after three days of my Data Science & Analytics course, I realise I was wrong. Really wrong. Excel is a spreadsheet tool used by millions of people from small business owners to data analysts at giant companies. And the best part? You don’t need to be a programmer to use it. You just need to know a few tricks. Here’s how Excel helps solve real-world problems using exactly what I learned in Week 1. 3 Real-World Ways Excel Is Used Business decisions with logic Managers use IF() statements to answer yes/no questions. Example: =IF(Sales>1000, "Bonus", "Needs Improvement"). One cell can decide who gets paid more. Cleaning messy data Real data is never clean. Marketing teams use Remove Duplicates, Find & Replace, and Text to Columns to fix hundreds of messy rows in seconds. No manual typing. Tracking deadlines and ages HR teams use DATEDIF() to calculate employee ages or years of service. TODAY() and NOW() keep reports automatically updated. No more “oh, I forgot to update the date.” 3 Excel Features I Learned This Week Remove Duplicates – One click, and Excel deletes repeated rows. Saved me from sending the same customer email twice. IFERROR() – Hides ugly errors like #DIV/0! and shows something friendly instead (e.g., “Check data”). Your boss will thank you. Sort & Filter – With AutoFilter, I can find all sales above $500 in one second. Then Custom Sort lets me sort by date and region together. My personal reflection Honestly? Learning Excel has changed how I see data. I used to look at a messy spreadsheet and feel lost. Now I see Remove Duplicates, Text to Columns, and TRIM() as tiny tools that bring order to chaos. Data isn’t scary anymore. It’s just a puzzle and Excel gives me the pieces. I’m only one week in. But I already feel like a junior data analyst in training.

Data analysis is at the heart of how we spot patterns and improve systems today. Tools like Python, SQL, Power BI, and Tableau are everywhere in the data world, but Excel has held its ground as the starting point for anyone getting into data work, and there is a reason for that. What is Excel? Excel is a spreadsheet built on a grid of rows and columns. You use it to organize, format, and calculate data. For analysts it is where messy raw data gets sorted out, numbers get worked through, and everything gets turned into something that actually makes sense to look at. Ways Excel is Used in Real-World Data Analysis 1. Data Cleaning Raw data is almost never clean. Names are misspelled, IDs get duplicated, spacing is off, values go missing. None of that is unusual, it is just the reality of working with real data. Before any analysis happens the data has to be honest, because if the data is wrong the results will be too. Functions like PROPER() and TRIM() are some of the basic tools that help get data into a state where you can actually work with it. 2. Financial Reporting Every business, big or small, needs to know where the money is going. Excel makes that straightforward. SUM() adds up a range of numbers, AVERAGE() finds the mean, and once the calculations are done the data can be turned into charts and dashboards that tell the story of the business clearly. Not everyone in the room is an analyst, but everyone can read a chart. 3. Business Decision Making Clean data presented well becomes a decision making tool. What do customers want? What is working? What needs to change? Sorting figures from highest to lowest or filtering by region can take thousands of rows and turn them into something focused and answerable. That is really what data is for, helping people make better calls. Excel Features I Have Learned and How They Apply Three features that have stood out to me are conditional formatting, data validation, and cell referencing. Conditional formatting highlights ce

Plug an oscilloscope probe into almost any real circuit and the trace will be fuzzy. Riding on top of the signal you actually want is a haze of higher-frequency noise — switching hash, radio pickup, digital crosstalk. The signal and the noise occupy different parts of the frequency spectrum, and that separation is an opportunity. If you can build something that passes the low frequencies and quietly turns down the high ones, the fuzz disappears and the signal stays. That something is a low-pass filter, and in its simplest form it is just a resistor and a capacitor. This article explains where the cut-off frequency comes from, works a concrete RC example, and clears up the misunderstandings that most often trip up a first filter design. Why this calculation matters Low-pass filters are everywhere a clean signal is needed. They sit in front of analog-to-digital converters as anti-aliasing filters, smooth the ripple out of power supplies, condition sensor outputs, and recover audio from a noisy line. Even an averaging operation in software is a low-pass filter wearing different clothes. The calculation matters because the cut-off frequency is a design decision with real consequences in both directions. Set it too low and you blur the signal you were trying to protect — its fast edges and genuine high-frequency content vanish along with the noise. Set it too high and the noise sails straight through. The cut-off is a deliberate line drawn through the frequency spectrum, and a passive RC filter places it with just two component values. The core formula A first-order RC low-pass filter is a resistor in series with the signal and a capacitor from the output node to ground. At low frequencies the capacitor is effectively an open circuit, so the output simply follows the input. At high frequencies the capacitor's impedance becomes small, shorting the high-frequency content to ground. The crossover between those two regimes is the cut-off frequency: f_c = 1 / ( 2 * pi * R * C

Those ousted included ADA journal editor-in-chief Steven Kahn and former ADA president Desmond Schatz

Alzheimer's disease affects over 55 million people worldwide, yet the precise molecular changes happening inside individual brain cells remain poorly understood. I wanted to dig into that question - not at the tissue level, but at single-cell resolution. So I built a full scRNA-seq analysis pipeline in Python using Scanpy, working with a publicly available dataset of 63,608 nuclei from human prefrontal cortex tissue (sourced from CZ CELLxGENE). The donors spanned three Braak stages: 0 (cognitively normal), 2 (early Alzheimer's), and 6 (severe Alzheimer's). Here's what I found and how I found it. The Dataset The data came from a study on the molecular characterisation of selectively vulnerable neurons in AD. It covers the superior frontal gyrus, a prefrontal region known to be hit hard by neurodegeneration - and includes seven major brain cell types: Glutamatergic neurons GABAergic neurons Oligodendrocytes OPCs (oligodendrocyte precursor cells) Astrocytes Microglia Endothelial cells 31,997 genes. 63,608 cells. Three disease stages. A lot to work with. The Pipeline 1. Quality Control No dataset is clean out of the box. I filtered cells to keep only those with between 200 and 6,000 detected genes, and excluded anything with more than 20% mitochondrial gene content (high mitochondrial reads usually signal a dying or damaged cell). This removed around 2,809 low-quality cells. 2. Normalisation Library sizes were normalised to 10,000 counts per cell, followed by log1p transformation, standard practice that makes cells comparable regardless of how deeply they were sequenced. I then identified 5,607 highly variable genes to focus the downstream analysis. 3. Dimensionality Reduction PCA (50 components) → neighbourhood graph (10 neighbours, 20 PCs) → UMAP embedding. The UMAP is where the biology starts to become visible. All seven cell types separated into distinct clusters, with clear separation between neuronal subtypes and glial populations. 4. Differential Expression For t

This week's science news.