--While most micro -robots face difficulty traveling on rugged and uneven terrain, b eetles can walk smoothly on the complex substrate without slipping or getting stuck o n the surface due to their stiffness-variable tarsi and expandable hooks on the tip of tarsi. In this study, we found that beetles actively bent and expand ed their claws regularly to crawl freely on mesh surfaces. Inspired by the crawling mechanism of the beetles, we designed an 8 -cm miniature climbing robot equipping artificial claw s to open and bend in the same cyclic manner as natural beetles. The robot can climb freely with a controllable gait on the mesh surface, steep incline of the angle of 60, and even transition surface. To our best knowledge, this is the first micro -scale robot that can climb both the mesh surface and cliffy incline. Their small size, lightweight, and strong navigation capabilities allow them to be deployed in complicated environments quickly. Numerous insect -scale robots have been developed with diversiform locomotion modes, including crawling [1-3], rolling [4-6], jumping[7-9], gliding [10, 11], and flying [12-14]. The actuators are diverse from traditional motor s [15] and pneumatic [16] to shape memory alloy [17], piezoelectric ceramics [18], and dielectric elastomer [19]. However, they can only locomote on a nearly level surface, which makes them unable to overcome barriers several times larger than their body size.

Police forces around the world are adding AI-powered robots. In a groundbreaking fusion of nature and technology, researchers at the University of Queensland have developed remote-controlled beetles equipped with tiny, removable backpacks that could drastically reduce the time it takes to locate survivors in disaster zones. Also known as cyborg beetles, these hybrid helpers are part of an ambitious project to improve emergency response in situations like building collapses, earthquakes or industrial explosions. By combining natural mobility with simple controls, researchers are developing a faster, more flexible way to reach people in hard-to-access areas. A close-up of a cyborg beetle with mounted electronics.

Breakthroughs, discoveries, and DIY tips sent every weekday. Most insects have evolved to see the blue, green, and even ultraviolet spectrums. But most insects have trouble parsing one hue in particular: red. Even bees and other pollinators that visit traditionally vibrant poppies aren't attracted by the visible coloration, but by the UV light reflected from their petals. Now, an international zoology team has discovered that some insect species can manage to see what their relatives cannot.

To keep global temperatures from rising more than 1.5 degrees Celsius, we need to cut emissions in half by 2035--even as we will likely hit another record for burning fossil fuels this year. Still, the brilliant engineering demonstrated in this year's winning projects provides hope that we can rise to the challenge. A new kind of thermal battery will allow us to decarbonize the heat that powers the industrial processes behind everything from cement to chemicals. Newly inexpensive lasers are helping turn ore into pure iron for steelmaking using renewable electricity. Food challenges have generated different types of innovation: Instead of hauling agricultural waste to decompose in the dump, why not create a harvester-style robot that can process it into carbon-sequestering, soil-enriching biochar? To fight pests, a technique called mRNA interference allows bioengineers to create a precision poison for a particularly troublesome beetle.

In 1988, we launched the Best of What's New Awards. The original list highlighted "the very things that make our lives more comfortable, more rewarding, more exciting, and more fun," to quote then-Publisher Grant A. Burnett. Now, in 2024, we continue our decades-old tradition of honoring big ideas. We even see hints of our original honorees in this year's list: Sea-Doo and Ford made both lists, 36 years apart. We're proud to bring you promising innovations--from things that make life at home easier to literal out-of-this-world explorations. This is the Best of What's New 2024. Had you asked me at the beginning of 2024 what our best gadgets list would look like, I'd have guessed it would be filled with quirky AI-driven devices like the rabbit R1 or the Humane Ai Pin. "Now with AI" is a phrase that has dominated consumer electronics in the 2020s. These devices promised unadulterated access to the power of neural networks in ways that would seamlessly integrate into our lives without relying on phones or smart fridges. Then, the devices came out. The software is slow and buggy, and the hardware is clunky. Maybe the stand-alone AI device will still have its year, and we'll look back and chuckle at these humble beginnings. In reality, 2024's big breakthrough came from Apple in the form of its long-rumored Vision Pro headset. The device has its own hurdles to clear, but after just a few minutes of using it, it was clear that it's something different, important, and honestly pretty amazing. The list also includes Sony's innovative pro-grade camera, the most accessible drone we've ever used, and a no-fun phone--no fun in a good way, of course. Credible rumors of Apple's VR bounced around the gadget blogs and tech sites for nearly a decade. It was consumer tech's sasquatch in that people claimed to have seen it, but no one knew if it even existed. Then, the Vision Pro emerged from the proverbial forest in February with a surprising design and a massive 3,500 price tag. It also came toting a new R-series chip and a dedicated OS meant for spatial computing.

Birds, bats and many insects can tuck their wings against their bodies at rest and deploy them to power flight. Whereas birds and bats use well-developed pectoral and wing muscles and tendons, how insects control these movements remains unclear, as mechanisms of wing deployment and retraction vary among insect species. Beetles (Coleoptera) display one of the most complex wing mechanisms. For example, in rhinoceros beetles, the wing deployment initiates by fully opening the elytra and partially releasing the hindwings from the abdomen. Subsequently, the beetle starts flapping, elevates the hindwings at the bases, and unfolds the wingtips in an origami-like fashion. Whilst the origami-like fold have been extensively explored, limited attention has been given to the hindwing base deployment and retraction, which are believed to be driven by thoracic muscles. Using high-speed cameras and robotic flapping-wing models, here we demonstrate that rhinoceros beetles can effortlessly elevate the hindwings to flight position without the need for muscular activity. We show that opening the elytra triggers a spring-like partial release of the hindwings from the body, allowing the clearance needed for subsequent flapping motion that brings the hindwings into flight position. The results also show that after flight, beetles can leverage the elytra to push the hindwings back into the resting position, further strengthening the hypothesis of a passive deployment mechanism. Finally, we validate the hypothesis with a flapping microrobot that passively deploys its wings for stable controlled flight and retracts them neatly upon landing, which offers a simple yet effective approach to the design of insect-like flying micromachines.

In this paper, we consider Bayesian reinforcement learning (BRL) where actions incur costs in addition to rewards, and thus exploration has to be constrained in terms of the expected total cost while learning to maximize the expected longterm total reward. In order to formalize cost-sensitive exploration, we use the constrained Markov decision process (CMDP) as the model of the environment, in which we can naturally encode exploration requirements using the cost function. We extend BEETLE, a model-based BRL method, for learning in the environment with cost constraints. We demonstrate the cost-sensitive exploration behaviour in a number of simulated problems.

They flag offensive content on social networks and delete spam from our inboxes. At the hospital, they help convert patient--doctor conversations into insurance billing codes. Sometimes, they alert law enforcement to potential terrorist plots and predict (poorly) the threat of violence on social media. Legal professionals use them to hide or discover evidence of corporate fraud. Students are writing their next school paper with the aid of a smart word processor, capable not just of completing sentences, but generating entire essays on any topic.

This paper provides a comprehensive review of past and current advances in the early detection of bark beetle-induced tree mortality from three primary perspectives: bark beetle & host interactions, RS, and ML/DL. In contrast to prior efforts, this review encompasses all RS systems and emphasizes ML/DL methods to investigate their strengths and weaknesses. We parse existing literature based on multi- or hyper-spectral analyses and distill their knowledge based on: bark beetle species & attack phases with a primary emphasis on early stages of attacks, host trees, study regions, RS platforms & sensors, spectral/spatial/temporal resolutions, spectral signatures, spectral vegetation indices (SVIs), ML approaches, learning schemes, task categories, models, algorithms, classes/clusters, features, and DL networks & architectures. Although DL-based methods and the random forest (RF) algorithm showed promising results, highlighting their potential to detect subtle changes across visible, thermal, and short-wave infrared (SWIR) spectral regions, they still have limited effectiveness and high uncertainties. To inspire novel solutions to these shortcomings, we delve into the principal challenges & opportunities from different perspectives, enabling a deeper understanding of the current state of research and guiding future research directions.

Automated Short Answer Grading (ASAG) has been an active area of machine-learning research for over a decade. It promises to let educators grade and give feedback on free-form responses in large-enrollment courses in spite of limited availability of human graders. Over the years, carefully trained models have achieved increasingly higher levels of performance. More recently, pre-trained Large Language Models (LLMs) emerged as a commodity, and an intriguing question is how a general-purpose tool without additional training compares to specialized models. We studied the performance of GPT-4 on the standard benchmark 2-way and 3-way datasets SciEntsBank and Beetle, where in addition to the standard task of grading the alignment of the student answer with a reference answer, we also investigated withholding the reference answer. We found that overall, the performance of the pre-trained general-purpose GPT-4 LLM is comparable to hand-engineered models, but worse than pre-trained LLMs that had specialized training.