A piece of the UFC White House event's setup is sitting in Pennsylvania Amish country Viral Ottawa Senators fan blamed for team's 0-2 playoff start banished to Taiwan Edward Cabrera's strikeout prop is the play as struggling Phillies face surging Cubs today Nuggets vs Timberwolves Game 3 pick hinges on Jaden McDaniels calling out Denver's entire defense Charles Barkley was disgusted by Magic's highly questionable pregame handshake ChatGPT predicted the first round of the NFL Draft and here's what it said Curt Cignetti was so focused this offseason, he turned down all external requests: 'I'm 95% football' Former MLB owner claims'despicable' San Francisco Giants are the reason the A's left Oakland Trump weighs in on Iran's internal power struggle and Strait of Hormuz control Hasan Piker justifies'social murder' of CEO Fox News celebrates'Bring Your Kids to Work Day' Trump says there's'no time frame' to secure Iran deal Iranian activist praises Trump's intervention after female protesters saved from execution Michael Brennan's ball found the greenside pond, but with teammate Johnny Keefer in Position A, he decided to go for it LIV Golf Is On Its Death Bed As The PGA Tour Wins The Golf WAR! | Don't @ Me w/ Dan Dakich Broadcasting legend Tim Brando joins Dan Dakich to break down the decline of LIV Golf, Bryson DeChambeau's unique success, and the flaws in modern Masters coverage. It's highly unlikely that Michael Brennan will be the only 24-year-old man to take his shirt off in public in New Orleans on Thursday, but he will be the only one to do so who has a PGA Tour victory under his belt. During the opening round of this week's Zurich Classic, a team event on Tour played at TPC Louisiana, Brennan and teammate Johnny Keefer began on the back nine and got things rolling early, getting to 4-under through their opening six holes. Michael Brennan of the United States catches a ball on the third green during the third round of the RBC Heritage 2026 at Harbour Town Golf Links on April 18, 2026, in Hilton Head Island, South Carolina. LPGA'S MAJOR CHAMPIONSHIP GREENSIDE PLUNGE POOL IS PREPOSTEROUS IN EVERY WAY After back-to-back pars on the 16th and 17th holes, the duo arrived at the Par 5 closing hole, which is when things got messy.

-- We present a new millimeter-scale bimorph actuator for microrobotic applications, driven by feedforward controlled shape-memory alloy (SMA) wires. The device weighs 10 mg, measures 14 mm in length, and occupies a volume of 4.8 mm The experimentally measured operational bandwidth is on the order of 20 Hz, and the unimorph and bimorph maximum low-frequency displacement outputs are on the order of 3.5 and 7 mm, respectively. T o test and demonstrate the functionality and suitability of the actuator for microrobotics, we developed the Fish-&-Ribbon-Inspired Small Swimming Harmonic roBot (FRISSHBot). Loosely inspired by carangiformes, the FRISSHBot leverages fluid-structure interaction (FSI) phenomena to propel itself forward, weighs 30 mg, measures 34 mm in length, operates at frequencies of up to 4 Hz, and swims at speeds of up to 3.06 mm s This robot is the lightest and smallest swimmer with onboard actuation developed to date. The vision of insect-scale robotic swarms working in harmony with humans to complete essential tasks for society will become a reality only once critical challenges in microfabrication, sensing, actuation, power, and computation are solved. One of these challenges is the creation of lightweight microactuators with low power consumption and versatile functionality. Numerous advanced and novel mm-to-cm-scale microsystems have been developed during the past few years using predominantly piezoelectric [1]-[8], electromagnetic [9]-[12], dielectric-elastomer (DE) [13]- [16], rotational motor [17]-[20], and shape-memory alloy (SMA) [21]-[25] actuation technologies. While, in the aggregate, these results represent innovation and progress in microrobotic design, rapid prototyping, control performance, autonomy, and energy efficiency, all the platforms presented in [1]-[20] are limited by the need for complex electronics and lack of sources of power with high energy densities. For obvious reasons, microactuators that require low operational power and simple electronics, generate high-force outputs, and exhibit high versatility are a superior choice for advanced autonomous microrobotics. One promising technological path in this direction is SMA-based actuation of the type presented in [21]-[25], which exhibits high-work densities (HWD) and requires low voltages of operation-- typically, 1 to 25 V.

Optimizing the fuel cycle cost through the optimization of nuclear reactor core loading patterns involves multiple objectives and constraints, leading to a vast number of candidate solutions that cannot be explicitly solved. To advance the state-of-the-art in core reload patterns, we have developed methods based on Deep Reinforcement Learning (DRL) for both single- and multi-objective optimization. Our previous research has laid the groundwork for these approaches and demonstrated their ability to discover high-quality patterns within a reasonable time frame. On the other hand, stochastic optimization (SO) approaches are commonly used in the literature, but there is no rigorous explanation that shows which approach is better in which scenario. In this paper, we demonstrate the advantage of our RL-based approach, specifically using Proximal Policy Optimization (PPO), against the most commonly used SO-based methods: Genetic Algorithm (GA), Parallel Simulated Annealing (PSA) with mixing of states, and Tabu Search (TS), as well as an ensemble-based method, Prioritized Replay Evolutionary and Swarm Algorithm (PESA). We found that the LP scenarios derived in this paper are amenable to a global search to identify promising research directions rapidly, but then need to transition into a local search to exploit these directions efficiently and prevent getting stuck in local optima. PPO adapts its search capability via a policy with learnable weights, allowing it to function as both a global and local search method. Subsequently, we compared all algorithms against PPO in long runs, which exacerbated the differences seen in the shorter cases. Overall, the work demonstrates the statistical superiority of PPO compared to the other considered algorithms.

The functional replication and actuation of complex structures inspired by nature is a longstanding goal for humanity. Creating such complex structures combining soft and rigid features and actuating them with artificial muscles would further our understanding of natural kinematic structures. We printed a biomimetic hand in a single print process comprised of a rigid skeleton, soft joint capsules, tendons, and printed touch sensors. We showed it's actuation using electric motors. In this work, we expand on this work by adding a forearm that is also closely modeled after the human anatomy and replacing the hand's motors with 22 independently controlled pneumatic artificial muscles (PAMs). Our thin, high-strain (up to 30.1%) PAMs match the performance of state-of-the-art artificial muscles at a lower cost. The system showcases human-like dexterity with independent finger movements, demonstrating successful grasping of various objects, ranging from a small, lightweight coin to a large can of 272g in weight. The performance evaluation, based on fingertip and grasping forces along with finger joint range of motion, highlights the system's potential.

HILTON HEAD ISLAND, S.C. (WSAV) – A new high-tech "smart fitness studio" has opened in the Lowcountry that relies on artificial intelligence and robotics instead of dumbbells and treadmills. Exercise Coach's personalized programs are optimized for efficiency resulting in 20-minute workouts per week. To help make the most of workouts, machines adjust to fit each gymgoer's strengths and weaknesses. The studio creates a unique experience by blending personalized strength and interval training within each session. Owners say the experience can work for anyone looking to get in shape.

Every afternoon Flora, 9, and Kate, 10, turn on their laptops and iPads to collaborate on a play called "World War III," a futuristic tale of two sisters who try to save the world after being blown back in time by a bomb. The close friends, who live a couple miles apart in St. Paul, Minnesota, used to hang out together to dream up dialogue and plot twists. Now, separated by coronavirus social distancing measures, they Skype on one screen and, on the other, type in a Google doc. No longer able to meet up with friends at the movies or the mall, Flora's brother Brodie, 15, stays in touch on FaceTime and Snapchat and through online games Minecraft and Rainbow Six Siege. He says communicating online with high school pals helps him cope with real-world worries about the coronavirus.

Nearly a decade into his life sentence for murder, Lydell Grant was escorted out of a Texas prison in November with his hands held high, free on bail, all thanks to DNA re-examined by a software program. "The last nine years, man, I felt like an animal in a cage," Grant, embracing his mother and brother, told the crush of reporters awaiting him in Houston. "Especially knowing that I didn't do it." Now, Grant, 42, is on a fast-track to exoneration after a judge recommended in December that Texas' highest criminal court vacate his conviction. His attorneys are hopeful a ruling is made in the coming weeks.

Nearest neighbor classification assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with finite samples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. We propose a locally adaptive nearest neighbor classification method to try to minimize bias. We use a Chi-squared distance analysis to compute a flexible metric for producing neighborhoods that are elongated along less relevant feature dimensions and constricted along most influential ones. As a result, the class conditional probabilities tend to be smoother in the modified neighborhoods, whereby better classification performance can be achieved. The efficacy of our method is validated and compared against other techniques using a variety of real world data. 1 Introduction

Nearest neighbor classification assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with finite samples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. We propose a locally adaptive nearest neighbor classification method to try to minimize bias. We use a Chi-squared distance analysis to compute a flexible metric for producing neighborhoods that are elongated along less relevant feature dimensions and constricted along most influential ones. As a result, the class conditional probabilities tend to be smoother in the modified neighborhoods, whereby better classification performance can be achieved. The efficacy of our method is validated and compared against other techniques using a variety of real world data. 1 Introduction