Plastic waste is being discovered in increasingly remote locations around the world, from fresh Antarctic snow to the mountain air above the Pyrenees. According to the World Economic Forum, between 75 and 199 million tons of plastic are currently in our oceans. This ranges from large floating debris to microplastics, which form as the bigger pieces of waste break down. As a result, scientists and engineers are working hard to find new solutions to the global problem of plastic pollution. These include aquatic drones that can be programmed to scoop up floating debris from the surface of rivers, and buggies that use artificial intelligence (AI) to search for and pick up litter for use on beaches.

Want to join a company where doing good is what we do? The right data can make that happen. As a member of the Enterprise Data Solutions team, you'll have the opportunity to work with data assets and data professionals across the entire organization to improve efficiencies, increase profitability and better manage risk. You'll also perform analysis and develop data solutions to help solve real business problems rather than shortsighted quick fixes. Partnering with your teammates to understand project requirements and user insights, you'll continually focus on supporting Amica's strategic plan to create peace of mind and build enduring relationships.

Inspired by fireflies, MIT researchers have created soft actuators that can emit light in different colors or patterns. Fireflies that light up dusky backyards on warm summer evenings use their luminescence for communication -- to attract a mate, ward off predators, or lure prey. These glimmering bugs also sparked the inspiration of scientists at MIT. Taking a cue from nature, they built electroluminescent soft artificial muscles for flying, insect-scale robots. The tiny artificial muscles that control the robots' wings emit colored light during flight. This electroluminescence could enable the robots to communicate with each other.

As the global population has expanded over time, agricultural modernisation has been humanity's prevailing approach to staving off famine. A variety of mechanical and chemical innovations delivered during the 1950s and 1960s represented the third agricultural revolution. The adoption of pesticides, fertilisers and high-yield crop breeds, among other measures, transformed agriculture and ensured a secure food supply for many millions of people over several decades. Concurrently, modern agriculture has emerged as a culprit of global warming, responsible for one-third of greenhouse gas emissions, namely carbon dioxide and methane. Meanwhile, inflation on the price of food is reaching an all-time high, while malnutrition is rising dramatically.

Isolation of spacecraft microvibrations is essential for the successful deployment of instruments relying on high-precision pointing. Hexapod platforms represent a promising solution, but the difficulties associated with attaining desirable 3D dynamics within acceptable mass and complexity budgets have led to a minimal practical adoption. This paper addresses the influence of strut boundary conditions (BCs) on system-level mechanical disturbance suppression. Inherent limitations of the traditional all-rotational joint configuration are highlighted and shown to originate in link mass and rotational inertia. A pin-slider BC alternative is proposed and analytically proven to alleviate them in both 2D and 3D. The advantages of the new BC hold for arbitrary parallel manipulators and are demonstrated for several hexapod geometries through numerical tests. A configuration with favourable performance is suggested. Finally, a novel planar joint that allows the physical realisation of the proposed BC is described and validated. Consequently, this work enables the development of platforms for microvibration attenuation that do not require active control.

Integrity and trust are at the heart of humanity and allow for peaceful nations, bonds and trust between multiple parties. However, when that integrity is played with, many become overprotective, people cannot enjoy the common object and once trustful systems are tampered with, connections between communities are disrupted. On a global scale, counterfeit goods are where this integrity is played with the most. Counterfeit pharmaceuticals cause the deaths of millions in developing nations2, counterfeit batteries pose risks to everyday items bursting at any moment and overall, these goods cost the global market over one trillion dollars. Current solutions can easily be reverse-engineered and as the counterfeit epidemic surges, the world needs a solution to make counterfeit goods impossible. The integration of nanotags and harnessing the randomness and uniqueness of quantum dots allow for unclonable tags on each product. These tags are verified by the end-user through a deep learning algorithm. The tags are unclonable by any quantum computer let alone any attacker, ensuring the security of millions of lives and billions of dollars. The global market of counterfeit goods is currently $1.8 trillion and this number is only increasing. During the COVID-19 pandemic, a greater urgency for counterfeiting occurred as the global demand for medical supplies continued to increase (2). Not only does counterfeiting cost the global economy trillions of dollars, but it also results in fake pharmaceutical pills, costing millions of lives (2). To add on, 500 identity frauds happen on a daily basis, showcasing how counterfeit goods are reaching an epidemic level (10). In 2015, 10% of all luxury goods in Europe were counterfeit, and the number continues to increase (10).

In many reacting flow systems, the thermo-chemical state-space is known or assumed to evolve close to a low-dimensional manifold (LDM). Various approaches are available to obtain those manifolds and subsequently express the original high-dimensional space with fewer parameterizing variables. Principal component analysis (PCA) is one of the dimensionality reduction methods that can be used to obtain LDMs. PCA does not make prior assumptions about the parameterizing variables and retrieves them empirically from the training data. In this paper, we show that PCA applied in local clusters of data (local PCA) is capable of detecting the intrinsic parameterization of the thermo-chemical state-space. We first demonstrate that utilizing three common combustion models of varying complexity: the Burke-Schumann model, the chemical equilibrium model and the homogeneous reactor. Parameterization of these models is known a priori which allows for benchmarking with the local PCA approach. We further extend the application of local PCA to a more challenging case of a turbulent non-premixed $n$-heptane/air jet flame for which the parameterization is no longer obvious. Our results suggest that meaningful parameterization can be obtained also for more complex datasets. We show that local PCA finds variables that can be linked to local stoichiometry, reaction progress and soot formation processes.

The UK is set to ease data mining laws in a move designed to further boost its flourishing AI industry. We all know that data is vital to AI development. Tech giants are in an advantageous position due to either having existing large datasets or the ability to fund/pay for the data required. Most startups rely on mining data to get started. Europe has notoriously strict data laws.

Roadheader is an engineering robot widely used in underground engineering and mining industry. Interactive dynamics simulation of roadheader is a fundamental problem in unmanned excavation and virtual reality training. However, current research is only based on traditional animation techniques or commercial game engines. There are few studies that apply real-time physical simulation of computer graphics to the field of roadheader robot. This paper aims to present an interactive physically-based simulation system of roadheader robot. To this end, an improved multibody simulation method based on generalized coordinates is proposed. First, our simulation method describes robot dynamics based on generalized coordinates. Compared to state-of-the-art methods, our method is more stable and accurate. Numerical simulation results showed that our method has significantly less error than the game engine in the same number of iterations. Second, we adopt the symplectic Euler integrator instead of the conventional fourth-order Runge-Kutta (RK4) method for dynamics iteration. Compared with other integrators, our method is more stable in energy drift during long-term simulation. The test results showed that our system achieved real-time interaction performance of 60 frames per second (fps). Furthermore, we propose a model format for geometric and robotics modeling of roadheaders to implement the system. Our interactive simulation system of roadheader meets the requirements of interactivity, accuracy and stability.

Artificial intelligence (AI) is often presented in binary terms in both popular culture and political analysis. Either it represents the key to a futuristic utopia defined by the integration of human intelligence and technological prowess, or it is the first step toward a dystopian rise of machines. This same binary thinking is practiced by academics, entrepreneurs, and even activists in relation to the application of AI in combating climate change. The technology industry's singular focus on AI's role in creating a new technological utopia obscures the ways that AI can exacerbate environmental degradation, often in ways that directly harm marginalized populations. In order to utilize AI in fighting climate change in a way that both embraces its technological promise and acknowledges its heavy energy use, the technology companies leading the AI charge need to explore solutions to the environmental impacts of AI.