Despite public motivation to recycle, significant barriers hinder effective household recycling in the UK. Decentralised local authority waste management creates citizen confusion and "wishcycling" (disposing of non-recyclable items in recycling bins). The recent Simpler Recycling Policy further complicates this landscape by mandating new identification, sorting, and cleaning requirements that will require citizen guidance to ensure they understand how these will impact their recycling practices. This mixed methods study (surveys n=50, expert interviews, design activities) used the Value Proposition Canvas to identify citizen pain points: confusion about logos, logistical constraints, and information gaps about local requirements. We then developed an interactive prototype application providing location-specific guidance, visual sorting aids, and material-specific information to address these painpoints. Focus group evaluation showed the prototype improved recycling accuracy by 60 percent, with marked improvements in packaging assessment. Technology-enabled solutions grounded in user-centred design can measurably improve recycling behaviours and reduce contamination. However, such
Plastics recycling depends on everyday sorting practices and on how recycling services are communicated and experienced. Virtual reality (VR) can present these practices and services in situated, interactive form, yet its role in service design for plastics recycling is still emerging. This paper examines how VR tools can contribute to designing plastics recycling services through two application cases that address different stages of the recycling journey. The first case, Clean Cabin Escape, is a household scale VR escape room where players collect and sort waste items into locally relevant categories, with immediate feedback that supports practice with plastics recycling decisions. The second case is a VR simulation of a plastics recycling center that represents a real planned site and is used in service design workshops where stakeholders explore layout, signage and customer paths for plastics fractions. Across the cases, we analyse how VR supported learning, engagement and shared sensemaking, and how it interacted with other service design methods such as workshops, customer path mapping and physical artefacts. The findings show that VR can make domestic sorting tasks and compl
The global shift towards electric vehicles (EVs) as a sustainable alternative to traditional gasoline-powered cars has triggered a significant rise in the demand for lithium-ion batteries. However, as the adoption of EVs grows, the issue of battery disposal and recycling has emerged as a critical challenge. The recycling of EV batteries is essential not only for reducing the environmental impact of battery waste but also for ensuring the sustainable supply of critical raw materials such as lithium, cobalt, and nickel. This paper explores recent innovations in the field of electric vehicle battery recycling, examining advanced techniques such as direct recycling, hydrometallurgical processes, and sustainable battery design. It also highlights the role of policy and industry collaboration in improving recycling infrastructure and addressing the economic and environmental challenges associated with battery waste. By focusing on both the technical and regulatory aspects of EV battery recycling, this paper aims to provide a comprehensive overview of the state of the industry and the future outlook for recycling technologies, ultimately paving the way for a cleaner, more sustainable futu
Electronic waste (e-waste) is one of the fastest-growing waste streams worldwide due to rapid technological advancements and shorter device lifespans. Improper disposal releases hazardous substances that harm the environment and human health, while valuable materials such as gold, copper, and aluminum are lost if not recycled. In 2022, approximately 62 million metric tonnes of e-waste were generated globally, but only about 22% was formally recycled. India generated around 1.751 million metric tonnes in 2023-24, with only 43% processed through authorized channels. Green Grid is a full-stack web-based platform designed to simplify and encourage e-waste recycling through an E-Dumper Locator, Green Rewards System, Insights and Awareness Hub, Scheduled Pickup Service, Recycling Impact Calculator, Eco AI Assistant, and Eco-Marketplace. Developed using React.js, Node.js, Express.js, SQL, Google Maps API, and JWT authentication, the platform transforms e-waste recycling into a transparent, educational, and rewarding process. By combining technology, awareness, and incentives, Green Grid promotes responsible disposal and supports circular economy practices for a more sustainable future.
We apply the combined technique of postselected amplification and photon-recycling to an optical setup of magnetic field precision measurement. We propose two recycling schemes and carry out analytic expressions for the amplified signal and measurement sensitivity. The results show significant improvement of performance over conventional measurement. The underlying reason is twofold. On one aspect, introducing the technique of recycling eliminates the shortcoming of data discarding in postselection, thus maintains similar noise level of conventional measurement (without postselection). On the other aspect, performing intentional postselection within the recycling framework, which was originally proposed in the context of gravitational wave detection, can amplify the signal. Thus, the measurement signal-to-noise ratio is enhanced.
This article examines household plastic recycling in Finland through two qualitative studies and four design concepts. Study 1 reports short interviews with residents about how they store, sort, and dispose of plastic packaging in their homes. The findings highlight recurring frictions: limited space, improvised storage, uncertainty about correct sorting, and difficulties with bulky or dirty items. Study 2 focuses on laundry detergent packaging as a common source of large plastic containers. Participants' purchase decisions prioritised price and cleaning performance, while expressing concern for environmental impact and confusion about materials, rinsing, and recyclability. Building on these insights, four student groups designed interactive recycling concepts that combine physical bins or bags with mobile applications. The concepts explore modular storage, sensing and compaction, playful feedback, and reward schemes to support domestic recycling routines. Together, the studies and concepts point to design opportunities at the intersection of packaging, home infrastructure, and digital services, while also raising questions about feasibility, privacy, and the cost of new devices.
Computing matrix pseudospectra over a prescribed region requires evaluating the smallest singular value of $C-zI$ at a large number of grid points, which can be prohibitively expensive for large-scale matrices. We develop a recycling-based framework for accelerating such computations for both dense and sparse matrices. The main idea is to exploit the correlation between singular value problems at neighboring grid points by adaptively recycling singular subspaces computed at previously visited points by an iterative SVD solver. We develop fast Rayleigh-Ritz-SVD procedures for extracting Ritz singular pairs from the recycled singular subspaces, together with fast residual evaluation procedures, with an overall cost that scales linearly with the number of recycled samples. When the iterative SVD solver admits preconditioning, we propose using a two-level preconditioner whose projection subspaces are recycled. Numerical experiments demonstrate that the proposed recycling strategies yield substantial speedups over existing methods while maintaining the accuracy of the computed pseudospectra.
The adoption of renewable energy resources, such as solar power, is on the rise. However, the excessive installation and lack of recycling facilities pose environmental risks. This paper suggests a circular economy approach to address the issue. By implementing blockchain technology, the end-of-life (EOL) of solar panels can be tracked, and responsibilities can be assigned to relevant stakeholders. The degradation of panels can be monetized by tracking users' energy-related activities, and these funds can be used for future recycling. A new coin, the recycling coin (RC-Coin), incentivizes solar panel recycling and utilizes decentralized finance to stabilize the coin price and supply issue.
Debt recycling is an aggressive equity extraction strategy that potentially permits faster repayment of a mortgage. While equity progressively builds up as the mortgage is repaid monthly, mortgage holders may obtain another loan they could use to invest on a risky asset. The wealth produced by a successful investment is then used to repay the mortgage faster. The strategy is riskier than a standard repayment plan since fluctuations in the house market and investment's volatility may also lead to a fast default, as both the mortgage and the liquidity loan are secured against the same good. The general conditions of the mortgage holder and the outside market under which debt recycling may be recommended or discouraged have not been fully investigated. In this paper, to evaluate the effectiveness of traditional monthly mortgage repayment versus debt recycling strategies, we build a dynamical model of debt recycling and study the time evolution of equity and mortgage balance as a function of loan-to-value ratio, house market performance, and return of the risky investment. We find that the model has a rich behavior as a function of its main parameters, showing strongly and weakly succe
The electric vehicle (EV) battery supply chain will face challenges in sourcing scarce and expensive minerals required for manufacturing and in disposing of hazardous retired batteries. Integrating recycling technology into the supply chain has the potential to alleviate these issues; however, players in the battery market must design investment plans for recycling facilities. In this paper, we propose a multistage stochastic optimization model for computing minimum cost recycling capacity decisions, in which retired batteries are recycled and recovered materials are used to manufacture new batteries. We transform EverBatt, a leading evaluation framework for battery recycling cost and environmental impact, into a prescriptive decision-making tool for determining optimal investment strategies. Our model is a separable concave minimization subject to linear constraints, a class for which we design a new finitely convergent global optimization algorithm that solves up to 14x faster than comparable algorithms. We propose an equivalent reformulation of the model that reduces the total number of variables by introducing integrality constraints, drastically reducing solve times. We detail
Krylov subspace recycling is a process for accelerating the convergence of sequences of linear systems. Based on this technique, the recycling BiCG algorithm has been developed recently. Here, we now generalize and extend this recycling theory to BiCGSTAB. Recycling BiCG focuses on efficiently solving sequences of dual linear systems, while the focus here is on efficiently solving sequences of single linear systems (assuming non-symmetric matrices for both recycling BiCG and recycling BiCGSTAB). As compared with other methods for solving sequences of single linear systems with non-symmetric matrices (e.g., recycling variants of GMRES), BiCG based recycling algorithms, like recycling BiCGSTAB, have the advantage that they involve a short-term recurrence, and hence, do not suffer from storage issues and are also cheaper with respect to the orthogonalizations. We modify the BiCGSTAB algorithm to use a recycle space, which is built from left and right approximate invariant subspaces. Using our algorithm for a parametric model order reduction example gives good results. We show about 40% savings in the number of matrix-vector products and about 35% savings in runtime.
Massive parameters of LLMs have made inference latency a fundamental bottleneck. Speculative decoding represents a lossless approach to accelerate inference through a guess-and-verify paradigm. Some methods rely on additional architectures to guess draft tokens, which need extra training before use. Alternatively, retrieval-based training-free techniques build libraries from pre-existing corpora or by n-gram generation. However, they face challenges like large storage requirements, time-consuming retrieval, and limited adaptability. Observing that candidate tokens generated during the decoding process are likely to reoccur in future sequences, we propose Token Recycling. It stores candidate tokens in an adjacency matrix and employs a breadth-first-search (BFS)-like algorithm to construct a draft tree, which is then validated through tree attention. New candidate tokens from the decoding process are then used to update the matrix. Token Recycling requires \textless2MB of additional storage and achieves approximately 2x speedup across all sizes of LLMs. It significantly outperforms existing train-free methods by 30\% and even a widely recognized training method by 25\%.
For many applications involving a sequence of linear systems with slowly changing system matrices, subspace recycling, which exploits relationships among systems and reuses search space information, can achieve huge gains in iterations across the total number of linear system solves in the sequence. However, for general (i.e., non-identity) shifted systems with the shift value varying over a wide range, the properties of the linear systems vary widely as well, which makes recycling less effective. If such a sequence of systems is embedded in a nonlinear iteration, the problem is compounded, and special approaches are needed to use recycling effectively. In this paper, we develop new, more efficient, Krylov subspace recycling approaches for large-scale image reconstruction and restoration techniques that employ a nonlinear iteration to compute a suitable regularization matrix. For each new regularization matrix, we need to solve regularized linear systems, ${\bf A} + γ_\ell {\bf E}_k$, for a sequence of regularization parameters, $γ_\ell$, to find the optimally regularized solution that, in turn, will be used to update the regularization matrix. In this paper, we analyze system and
With the rapid surge in the number of on-road Electric Vehicles (EVs), the amount of spent lithium-ion (Li-ion) batteries is also expected to explosively grow. The spent battery packs contain valuable metal and materials that should be recovered, recycled, and reused. However, only less than 5% of the Li-ion batteries are currently recycled, due to a multitude of challenges in technology, logistics and regulation. Existing battery recycling is performed manually, which can pose a series of risks to the human operator as a consequence of remaining high voltage and chemical hazards. Therefore, there is a critical need to develop an automated battery recycling system. In this paper, we present ABatRe-sim, an open-source robotic battery recycling simulator, to facilitate the research and development in efficient and effective battery recycling au-omation. Specifically, we develop a detailed CAD model of the battery pack (with screws, wires, and battery modules), which is imported into Gazebo to enable robot-object interaction in the robot operating system (ROS) environment. It also allows the simulation of battery packs of various aging conditions. Furthermore, perception, planning, an
Rapid economic growth gives rise to the urgent demand for a more efficient waste recycling system. This work thereby developed an innovative recycling bin that automatically separates urban waste to increase the recycling rate. We collected 1800 recycling waste images and combined them with an existing public dataset to train classification models for two embedded systems, Jetson Nano and K210, targeting different markets. The model reached an accuracy of 95.98% on Jetson Nano and 96.64% on K210. A bin program was designed to collect feedback from users. On Jetson Nano, the overall power consumption of the application was reduced by 30% from the previous work to 4.7 W, while the second system, K210, only needed 0.89 W of power to operate. In summary, our work demonstrated a fully functional prototype of an energy-saving, high-accuracy smart recycling bin, which can be commercialized in the future to improve urban waste recycling.
The techniques of power recycling and signal recycling have proven as key concepts to increase the sensitivity of large-scale gravitational wave detectors by independent resonant enhancement of light power and signal sidebands within the interferometer. Developing the latter concept further, twin signal recycling was proposed as an alternative to conventional detuned signal recycling. Twin signal recycling features the narrow-band sensitivity gain of conventional detuned signal recycling but furthermore facilitates the injection of squeezed states of light, increases the detector sensitivity over a wide frequency band and requires a less complex detection scheme for optimal signal readout. These benefits come at the expense of an additional recycling mirror, thus increasing the number of degrees of freedom in the interferometer which need to be controlled. In this article we describe the development of a length sensing and control scheme and its successful application to a tabletop-scale power recycled Michelson interferometer with twin signal recycling. We were able to lock the interferometer in all relevant longitudinal degrees of freedom, enabling the long-term stable operation
A Krylov subspace recycling method for the efficient evaluation of a sequence of matrix functions acting on a set of vectors is developed. The method improves over the recycling methods presented in [Burke et al., arXiv:2209.14163, 2022] in that it uses a closed-form expression for the augmented FOM approximants and hence circumvents the use of numerical quadrature. We further extend our method to use randomized sketching in order to avoid the arithmetic cost of orthogonalizing a full Krylov basis, offering an attractive solution to the fact that recycling algorithms built from shifted augmented FOM cannot easily be restarted. The efficacy of the proposed algorithms is demonstrated with numerical experiments.
As a way of saving quantum resources, recycling of Bell nonlocality has been experimentally studied, but restricted to sequential unsharp measurements. However, it has been theoretically shown recently that projective measurements are sufficient for recycling nonlocality [Phys. Rev. Lett. \textbf{129}, 230402 (2022)]. Here, we go beyond unsharp measurement scenarios and experimentally demonstrate the recycling of nonlocal resources with projective measurements. By verifying the violation of Clauser-Horne-Shimony-Holt (CHSH) inequality, we find that three independent parties can recycle the Bell nonlocality of a two-qubit state, whether it is maximally or partially entangled. Furthermore, in the double violation region, the optimal trade-off for partially entangled states can be 11 standard deviations better than that for maximally entangled states. Our results experimentally eliminate the common misconception that projective measurements are incompatible with the recycling of quantum correlations. In addition, our nonlocality recycling setup does not require entanglement assistance, which is much more experimentally friendly, thus paving the way for the reuse of other kinds of quan
Recent observations found close-in planets with significant atmospheres of hydrogen and helium in great abundance. These are the so-called super-Earths and mini-Neptunes. Their atmospheric composition suggests that they formed early during the gas-rich phase of the circumstellar disk and were able to avoid becoming hot Jupiters. As a possible explanation, recent studies explored the recycling hypothesis and showed that atmosphere-disk recycling is able to fully compensate for radiative cooling and thereby halt Kelvin-Helmholtz contraction to prevent runaway gas accretion. To understand the parameters that determine the efficiency of atmospheric recycling, we extend our earlier studies by exploring the effects of the core mass, the effect of circumstellar gas on sub-Keplerian orbits (headwind), and the optical depth of the surrounding gas on the recycling timescale. Additionally, we analyze their effects on the size and mass of the forming atmosphere. For the explored parameter space, all simulations eventually reach an equilibrium where heating due to hydrodynamic recycling fully compensates radiative cooling. In this equilibrium, the atmosphere-to-core mass ratio stays well below
Phobos is the target of the return sample mission Martian Moons eXploration by JAXA that will analyze in great details the physical and compositional properties of the satellite from orbit, from the surface and in terrestrial laboratories, giving clues about its formation. Some models propose that Phobos and Deimos were formed after a giant impact giving rise to an extended debris disk. Assuming that Phobos formed from a cascade of disruptions and re-accretions of several parent bodies in this disk, and that they are all characterized by a low material cohesion, Hesselbrock & Milton (2017) have showed that a recycling process may happen during the assembling of Phobos, by which Phobos' parents are destroyed into a Roche-interior ring and reaccreted several times. In the current paper we explore in details the recycling model, and pay particular attention to the characteristics of the disk using 1D models of disk/satellite interactions. In agreement with previous studies we confirm that, if Phobos' parents bodies are gravitational aggregates (rubble piles), then the recycling process does occur. However, Phobos should be accompanied today by a Roche-interior ring. Furthermore, t