The free Green's Dictionary of Slang celebrates 500 years of'vulgar tongue.' Breakthroughs, discoveries, and DIY tips sent six days a week. While some terms or phrases may linger for centuries, most of today's slang terminology is more current. That often makes it difficult to keep up with the times, let alone understand casual communications of the past. That's where Jonathon Green came to the rescue. In 1993, Green started compiling 500 years of English slang by sifting through mountains of primary sources.

It's not the worst solution to the issue, but the images are definitely AI. Breakthroughs, discoveries, and DIY tips sent six days a week. It's increasingly difficult to keep track of all the generative AI slop currently fooling unsuspecting internetgoers on any given day. Earlier this month, a (fake) image went viral after enough people genuinely believed North Carolina conservationists kept wild horses warm during winter storms by wrapping them in fiberglass insulation . Now, another hoax is tugging on the heartstrings of animal lovers.

How do birds stay warm in winter? Puffing up is just the start. Breakthroughs, discoveries, and DIY tips sent six days a week. Then you notice a robin hopping from branch to branch. Nearby, ducks are calmly swimming in the pond and waddling around on the ice.

Every day, 50,000 mattresses are tossed in the trash in the United States. A relative of penicillin could be the cure. Breakthroughs, discoveries, and DIY tips sent six days a week. Shopping for a new mattress can be stressful--this is something you plan to sleep on for years to come, after all. But your old one can be its own problem for the environment .

This paper develops a mathematically rigorous, philosophically grounded framework for evaluating artificial memory systems, rooted in the metaphysical structure of Leibniz's Monadology. Building on a previously formalized metric, the Artificial Age Score (AAS), the study maps twenty core propositions from the Monadology to an information-theoretic architecture. In this design, each monad functions as a modular unit defined by a truth score, a redundancy parameter, and a weighted contribution to a global memory penalty function. Smooth logarithmic transformations operationalize these quantities and yield interpretable, bounded metrics for memory aging, representational stability, and salience. Classical metaphysical notions of perception, apperception, and appetition are reformulated as entropy, gradient dynamics, and internal representation fidelity. Logical principles, including the laws of non-contradiction and sufficient reason, are encoded as regularization constraints guiding memory evolution. A central contribution is a set of first principles proofs establishing refinement invariance, structural decomposability, and monotonicity under scale transformation, aligned with the metaphysical structure of monads. The framework's formal organization is structured into six thematic bundles derived from Monadology, aligning each mathematical proof with its corresponding philosophical domain. Beyond evaluation, the framework offers a principled blueprint for building Al memory architectures that are modular, interpretable, and provably sound.

At the 1933 World's Fair in Chicago, an exhibit called The Home of Tomorrow laid out a vision of a futuristic living space. It included outlandish predictions including personal helicopter pads, but it also foreshadowed central air conditioning, automatic dishwashers, and other innovations that have become integral parts of modern life. As we envision the home of the future, it's easy to get caught up in sci-fi-inspired predictions of fully autonomous homes serviced by robotic butlers and disembodied virtual assistants that remove the humanity from our living spaces. Here at Popular Science, we reject that vision. For the inaugural Home of the Future Awards, we have selected 25 products that augment life at home by making it more efficient, affordable, accessible, and--ultimately--more enjoyable. We test, preview, and evaluate hundreds of products per year and these products deserve a chance to cohabitate with you and the people who matter to you. A flat top grill is one of the most versatile ways to cook just about anything from pancakes to burgers, but temperature control is key.

This study proposes a two-phase methodology for detecting and classifying auxiliary insulation in structural components. In the detection phase, a YOLOv8x model is trained on a dataset of complete structural blueprints, each annotated with bounding boxes indicating areas that should contain insulation. In the classification phase, these detected insulation patches are cropped and categorized into two classes: present or missing. These are then used to train a YOLOv8x-CLS model that determines the presence or absence of auxiliary insulation. Preprocessing steps for both datasets included annotation, augmentation, and appropriate cropping of the insulation regions. The detection model achieved a mean average precision (mAP) score of 82%, while the classification model attained an accuracy of 98%. These findings demonstrate the effectiveness of the proposed approach in automating insulation detection and classification, providing a foundation for further advancements in this domain.

Abstract-- The inspection of confined critical infrastructure such as attics or crawlspaces is challenging for human operators due to insufficient task space, limited visibility, and the presence of hazardous materials. This paper introduces a prototype of PARIS (Precision Application Robot for Inaccessible Spaces): a use-inspired teleoperated mobile robot manipulator system that was conceived, developed, and tested for--and selected as a Phase I winner of--the U.S. Department of Energy's E-ROBOT Prize. To improve the thermal efficiency of buildings, the PARIS platform supports: 1) teleoperated mapping and navigation, enabling the human operator to explore compact spaces; 2) inspection and sensing, facilitating the identification and localization of under-insulated areas; and 3) air-sealing targeted gaps and cracks through which thermal energy is lost. The resulting versatile platform can also be tailored for targeted application of treatments and remediation in constrained spaces. Approximately 75% of the world's greenhouse gas (GHG) emissions result from the cumulative energy sector [1].

"Noise" is a fuzzy word--a noisy one, in the statistical sense. Its meanings run the gamut from the negative to the positive, from the overpowering to the mysterious, from anarchy to sublimity. The negative seems to lie at the root: etymologists trace the word to "nuisance" and "nausea." Noise is what drives us mad; it sends the Grinch over the edge at Christmastime. ("Oh, the Noise! Noise!") Noise is the sound of madness itself, the din within our minds. The demented narrator of Poe's "The Tell-Tale Heart" jabbers about noise while he hallucinates his victim's heartbeat: "I found that the noise was not within my ears. . . . The noise steadily increased. . . . Yet noise can be righteous and majestic. The Psalms are full of joyful noise, noise unto the Lord. In the Book of Ezekiel, the voice of God is said to be "like a noise of many waters." In "Paradise Lost," Heaven makes "infernal noise" as it beats back the armies of Hell. At the same time, the word can summon all manner of ...

Buildings energy efficiency is a widely researched topic, which is rapidly gaining popularity due to rising environmental concerns and the need for energy independence. In Northern Europe heating energy alone accounts for up to 70 percent of the total building energy consumption. Industry 4.0 technologies such as IoT, big data, cloud computing and machine learning, along with the creation of predictive and proactive digital twins, can help to reduce this number. However, buildings thermal dynamics is a very complex process that depends on many variables. As a result, commonly used physics-based white box models are time-consuming and require vast expertise. On the contrary, black box forecasting models, which rely primarily on building energy consumption data, lack fundamental insights and hinder re-use. In this study we propose an architecture to facilitate grey box modelling of building thermal dynamics while integrating real time IoT data with 3D representation of buildings. The architecture is validated in a case study creating a digital twin platform that enables users to define the thermal dynamics of buildings based on physical laws and real data, thus facilitating informed decision making for the best heating energy optimization strategy. Also, the created user interface enables stakeholders such as facility managers, energy providers or governing bodies to analyse, compare and evaluate buildings thermal dynamics without extensive expertise or time resources.