Spain's unemployment rate in May

> When marketers start using "encryption jargon." Written by: Chloe@ Foresight News Imagine this: a traditional marketer attending a Web3 conference for the first time, their pupils shaking when they hear "TVL," "alpha groups," and "staking APY." That's right, this is the dimensional wall between old and new marketing languages. Why not compile a "Traditional MKT and Web3 Terminology Correspondence Table" as a "encryption translator," supplementing familiar PMF into Tokenomics Fit, turning A/B testing into an on-chain detective game, and even dressing "customer loyalty" in an NFT vest (now called Holder Loyalty). After all, in

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Next Generation Internet: Brain-Machine Surfing, Human-Machine On-Chain 🧠 AI is currently in full swing; however, there are not many breakthroughs at the technical level. Applications led by LLM interactive window robots are flourishing, but the AI field has entered a stage of large-scale engineering and commercial expansion, and it has reached a stagnation bottleneck at the theoretical level. Future assets and innovation hotspots will inevitably move towards brain-machine interfaces, new energy substitute materials, and the space economy. Core components of BCI: 🧠Signal Acquisition Invasive: Implanting electrodes (such as microelectrode arrays, ECoG) through surgery, high signal quality but with a risk of infection. Non-invasive: EEG (electroencephalography): records electrical activity through scalp electrodes, low cost but poor spatial resolution. MEG (magnetoencephalography): records magnetic field signals, high resolution but expensive equipment. fMRI (functional magnetic resonance imaging): indirectly measures neural activity through blood oxygen level dependent (BOLD) signals. fNIRS (functional near-infrared spectroscopy): detects changes in blood oxygen using light signals, portable but low temporal resolution. 🧠Signal Types Event-Related Potentials (ERP): such as P300 (positive wave appearing 300ms later), used for spelling systems. Sensory Evoked Potentials: such as Visual Evoked Potentials (VEP), Auditory Evoked Potentials (AEP). Sensorimotor Rhythm (SMR): generated by imagining limb movements, used to control prosthetics or cursors. 🧠Signal Processing Feature Extraction: Remove noise and extract useful information, common methods include: Common Spatial Pattern (CSP): Maximizing the variance difference between two types of signals (see formula below). Independent Component Analysis (ICA): Separating signal sources and removing artifacts (such as blink interference). Wavelet Transform (WT): Extracting time-frequency features. Classification Algorithms: Mapping features to control commands, common methods include: Support Vector Machine (SVM): Separating different categories through hyperplanes. Neural Networks (NN): Such as Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN). Fuzzy Inference System (FIS): Handling uncertain signals. Future research directions 1. Develop low-cost, high-resolution non-invasive devices (such as low-density EEG); 2. Combine high-performance deep learning algorithms (such as LSTM, Transformer) to improve classification accuracy. 3. Optimize real-time signal processing algorithms to reduce latency; 4. Expand application scenarios (such as emotion recognition, virtual reality control).

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