These beautiful and sturdy chef's knives make great gifts (for yourself or others), and are 20 percent off right now. As a home cook, I spent most of my life using dull, cheap knives. It wasn't until I started testing chef's knives earlier this year that I realized how important it is to invest a little bit in the tool you'll most likely be using every day for years (or even decades if you keep up with maintenance). I didn't realize that using a cheap, very dull knife as my daily driver was slowing down my cooking process, making less effective cuts, and even putting me at risk of injury, as the blade was dull and caused me to apply more pressure (which in turn made things more dangerous). Plus, they make seriously wonderful presents.

The chef's knife is the workhorse of the kitchen. We sliced, diced, and minced to find the best for every home chef. A Close Second Chef's Knife (Made From High-Carbon Stainless Steel) Zwilling Four Star 8-Inch Chef's Knife Not all knives are created equal, and a chef's knife is given that name for a reason. Like the proverbial dog to man, a chef needs their knife. Arguably the most important multipurpose tool you can find in a kitchen, it's the chef's main weapon--it can slice, dice, and chop ingredients with speed and precision. A chef's knife generally has a super-sharp end point and a curved, sloping edge. This curve is what makes the chef knife stand out, as it's designed to work with the natural rocking motion for quick chopping that also allows for finer cuts. With technology like ovens with cameras inside and AI-enabled refrigerators, the chef's knife remains the simple tool necessary for any kitchen.

This top-notch cookware will keep pumping out perfect food for years to come. We may earn revenue from the products available on this page and participate in affiliate programs. While many people use gas-powered appliances or convection cooking that heats up whatever is directly atop the burner, others pick induction cooktops --cooking surfaces with a copper coil that creates a magnetic field to heat the pan and food. However, these appliances need the proper cookware to achieve optimal results. Don't worry; we've got you covered.

There's no doubt Great Britain lays claim to some of the greatest scientific discoveries and inventions that have changed the face of modern society. Now, MailOnline's interactive map reveals the birthplace of 30 of these famous British marvels, from stainless steel to the jet engine and the electric motor. Who can forget Alan Turing's Bombe machine, used to break Enigma-enciphered messages about enemy military operations during WWII? Turing developed the Bombe in 1939 at Bletchley Park in Buckinghamshire and hundreds were built, marking a crucial contribution to the war effort. Also on the map is the hovercraft invented by Christopher Cockerell in 1955 and first launched four years later on the the Isle of Wight.

Elon Musk officially unveiled more futuristic Tesla devices last week, but it seems not everyone is thrilled. Australian-Egyptian filmmaker Alex Proyas has accused the billionaire tech boss of poaching his ideas from his 2004 film'I, Robot'. On X (Twitter), Proyas posted photos of futuristic tech from'I, Robot' next to three remarkably-similar Tesla products – Optimus, Robovan and Robotaxi. Proyas also included the message: 'Hey Elon, Can I have my designs back please?' Robovan and Robotaxi were unveiled on Thursday at a Tesla event dubbed'We Robot' – a blatant reference to the film. Alex Proyas posted photos from his 2004 film'I, Robot' (left) next to Tesla's remarkably similar designs (right) Tesla's Optimus has a striking resemblance to Sonny, the fictional robot protagonist from the movie, starring Will Smith (pictured) Set in Chicago in 2035, 'I, Robot' depicts intelligent robots filling public service positions in a dystopian world.

Achieving desired mechanical properties in additive manufacturing requires many experiments and a well-defined design framework becomes crucial in reducing trials and conserving resources. Here, we propose a methodology embracing the synergy between high-throughput (HT) experimentation and hierarchical machine learning (ML) to unveil the complex relationships between a large set of process parameters in Laser Powder Bed Fusion (LPBF) and selected mechanical properties (tensile strength and ductility). The HT method envisions the fabrication of small samples for rapid automated hardness and porosity characterization, and a smaller set of tensile specimens for more labor-intensive direct measurement of yield strength and ductility. The ML approach is based on a sequential application of Gaussian processes (GPs) where the correlations between process parameters and hardness/porosity are first learnt and subsequently adopted by the GPs that relate strength and ductility to process parameters. Finally, an optimization scheme is devised that leverages these GPs to identify the processing parameters that maximize combinations of strength and ductility. By founding the learning on larger easy-to-collect and smaller labor-intensive data, we reduce the reliance on expensive characterization and enable exploration of a large processing space. Our approach is material-agnostic and herein we demonstrate its application on 17-4PH stainless steel.

Photonic surfaces designed with specific optical characteristics are becoming increasingly important for use in in various energy harvesting and storage systems. , In this study, we develop a surrogate-based optimization approach for designing such surfaces. The surrogate-based optimization framework employs the Random Forest algorithm and uses a greedy, prediction-based exploration strategy to identify the laser fabrication parameters that minimize the discrepancy relative to a user-defined target optical characteristics. We demonstrate the approach on two synthetic benchmarks and two specific cases of photonic surface inverse design targets. It exhibits superior performance when compared to other optimization algorithms across all benchmarks. Additionally, we demonstrate a technique of inverse design warm starting for changed target optical characteristics which enhances the performance of the introduced approach.

Tesla CEO Elon Musk stands in front of the damaged Cybertruck after it fails a demonstration of its durability.Ringo H.W. Chiu / AP At a live delivery event this November, where Elon Musk awkwardly opened the door for about a dozen new Cybertruck owners, he told the world: "The apocalypse can come along any moment, and here at Tesla, we have the best in apocalypse technology." Then he showed a video of the vehicle being pummeled by a machine gun, quipping, "If you're ever in an argument with another car, you will win." And then he sold a bunch of Cybertrucks. Two million have been preordered--and 500 delivered--for over 60,000 a pop. Some soon proved that they couldn't survive a test drive, let alone a ride with Mad Max.

In this paper, we introduce the humanoid robot DRACO 3 by providing a high-level description of its design and control. This robot features proximal actuation and mechanical artifacts to provide a high range of hip, knee and ankle motion. Its versatile design brings interesting problems as it requires a more elaborate control system to perform its motions. For this reason, we introduce a whole body controller (WBC) with support for rolling contact joints and show how it can be easily integrated into our previously presented open-source Planning and Control (PnC) framework. We then validate our controller experimentally on DRACO 3 by showing preliminary results carrying out two postural tasks. Lastly, we analyze the impact of the proximal actuation design and show where it stands in comparison to other adult-size humanoids.

To the naked eye, a sheet of stainless steel presents a smooth, polished, homogenous surface. The same material when viewed at 400 times magnification reveals its true jumbled structure--different crystal shapes, joined at wildly different angles. Researchers at the University of Illinois Urbana-Champaign used data from high-resolution images of stainless-steel samples to train neural networks that make predictions about how the material will behave at places where the crystals meet, when strained. John Lambros explained, when studying the properties of a material such as stainless steel, it is impossible to conduct separate experiments at such high magnifications that subject it to every conceivable parameter--every temperature, every loading angle, every amount of pressure. So we often rely on models.