Life Cycle Assessment of a novel digestate treatment unit for anaerobic digestate plant: a UK case

Management of digestate co-product produced from anaerobic digestion (AD) has become a challenge due to impacts on the environment. Valorising AD into high-value products is not only considered as a solution to this issue but can also make AD more cost-effective. Project NOMAD (Novel Organic recovery using Mobile ADvanced technology) funded by H2020 is currently developing an innovative solution for valorising digestate. A designed mobile unit combines several digestate treatment technologies, i.e., solid-liquid separation, ultraviolet light and ozone oxidation, and electrodialysis. The valuable nutrients are concentrated from the liquid fraction, and the solid fraction is collected as compost. This study adopts Life Cycle Assessment (LCA) methodology to assess environmental impacts of the NOMAD unit incorporated into a UK AD plant, focusing on business-as-usual (BAU) case, NOMAD scenario, and upscaled NOMAD scenario. The BAU case is current management of digestate, where digestate is transported, stored, and applied to farmlands. The NOMAD scenario introduces one unit, capable of addressing digestate 5 ton/day, while the upscaled NOMAD scenario can process all digestate produced from the AD plant. 12 impact categories are selected using ReCiPe 2016. The results show that the upscaled NOMAD scenario can reduce 11%-69% of targeted impacts compared to BAU scenario, with 1%-24% reduction for NOMAD scenario. The NOMAD unit process, either upscaled or one-unit, contributes less than 6% of overall impacts, while AD activities and field application are the main impact contributors. The outcome of these scenarios validates the NOMAD unit for valorisation of digestate from environmental impact perspective

Mitigating Representation Bias in Action Recognition: Algorithms and Benchmarks

Deep learning models have achieved excellent recognition results on large-scale video benchmarks. However, they perform poorly when applied to videos with rare scenes or objects, primarily due to the bias of existing video datasets. We tackle this problem from two different angles: algorithm and dataset. From the perspective of algorithms, we propose Spatial-aware Multi-Aspect Debiasing (SMAD), which incorporates both explicit debiasing with multi-aspect adversarial training and implicit debiasing with the spatial actionness reweighting module, to learn a more generic representation invariant to non-action aspects. To neutralize the intrinsic dataset bias, we propose OmniDebias to leverage web data for joint training selectively, which can achieve higher performance with far fewer web data. To verify the effectiveness, we establish evaluation protocols and perform extensive experiments on both re-distributed splits of existing datasets and a new evaluation dataset focusing on the action with rare scenes. We also show that the debiased representation can generalize better when transferred to other datasets and tasks.Comment: ECCVW 202

Life Cycle Assessment of baby leaf spinach: Reduction of waste through interventions in growing treatments and packaging

Food production, distribution and waste impact significantly on the environment, with recognised contributions to GHG emissions and Global Warming Potential (GWP) at each stage of the supply chain. Fresh leaf vegetables, such as salad leaves and spinach, are particularly prone to spoilage and efforts are being made to reduce waste, by increasing shelf-life through growing treatments and packaging choices. This presentation reports on the findings of Life Cycle Assessment studies carried out to support the Science Foundation Ireland (SFI) funded project ‘Leaf no Waste’ (Grant: 20/FIP/FD/8934). The study looks at the production of baby leaf spinach grown in Ireland and explores changes in the environmental impacts profile (e.g. GWP) for a foliar silicon treatment and two packaging options (i.e. Oriented Polypropylene (OPP) and Polylactide (PLA)). The system boundary of the study includes the field production of spinach, storage, packaging, retail, and waste management options, for the functional unit 1 kg packed baby leaf spinach. 4 scenarios were selected from the experimental data, namely spinach packed in OPP, spinach packed in PLA, silicon treated spinach in OPP, and silicon treated spinach in PLA. Furthermore, waste at 3-day shelf-life and waste at 7-day shelf-life were compared, to evaluate the effects of the treatments, and the resulting environmental impacts based on the LCA . The preliminary results illustrate that the storage and packaging process and retail stage are among the key contributors to GWP due to packaging material production and energy use. Comparison of scenarios under 3-day shelf life shows that spinach with PLA packaging is worse than that with OPP packaging in both base case and silicon treatment scenarios. However, application of silicon product shows potential to benefit the spinach supply chain with PLA packaging, while it has little effect on OPP packaging cases

Research progress on the reduced neural repair ability of aging Schwann cells

Peripheral nerve injury (PNI) is associated with delayed repair of the injured nerves in elderly patients, resulting in loss of nerve function, chronic pain, muscle atrophy, and permanent disability. Therefore, the mechanism underlying the delayed repair of peripheral nerves in aging patients should be investigated. Schwann cells (SCs) play a crucial role in repairing PNI and regulating various nerve-repair genes after injury. SCs also promote peripheral nerve repair through various modalities, including mediating nerve demyelination, secreting neurotrophic factors, establishing Büngner bands, clearing axon and myelin debris, and promoting axon remyelination. However, aged SCs undergo structural and functional changes, leading to demyelination and dedifferentiation disorders, decreased secretion of neurotrophic factors, impaired clearance of axonal and myelin debris, and reduced capacity for axon remyelination. As a result, aged SCs may result in delayed repair of nerves after injury. This review article aimed to examine the mechanism underlying the diminished neural repair ability of aging SCs

LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching

The recent advancements in text-to-3D generation mark a significant milestone in generative models, unlocking new possibilities for creating imaginative 3D assets across various real-world scenarios. While recent advancements in text-to-3D generation have shown promise, they often fall short in rendering detailed and high-quality 3D models. This problem is especially prevalent as many methods base themselves on Score Distillation Sampling (SDS). This paper identifies a notable deficiency in SDS, that it brings inconsistent and low-quality updating direction for the 3D model, causing the over-smoothing effect. To address this, we propose a novel approach called Interval Score Matching (ISM). ISM employs deterministic diffusing trajectories and utilizes interval-based score matching to counteract over-smoothing. Furthermore, we incorporate 3D Gaussian Splatting into our text-to-3D generation pipeline. Extensive experiments show that our model largely outperforms the state-of-the-art in quality and training efficiency.Comment: The first two authors contributed equally to this work. Our code will be available at: https://github.com/EnVision-Research/LucidDreame

ReFlow-TTS: A Rectified Flow Model for High-fidelity Text-to-Speech

The diffusion models including Denoising Diffusion Probabilistic Models (DDPM) and score-based generative models have demonstrated excellent performance in speech synthesis tasks. However, its effectiveness comes at the cost of numerous sampling steps, resulting in prolonged sampling time required to synthesize high-quality speech. This drawback hinders its practical applicability in real-world scenarios. In this paper, we introduce ReFlow-TTS, a novel rectified flow based method for speech synthesis with high-fidelity. Specifically, our ReFlow-TTS is simply an Ordinary Differential Equation (ODE) model that transports Gaussian distribution to the ground-truth Mel-spectrogram distribution by straight line paths as much as possible. Furthermore, our proposed approach enables high-quality speech synthesis with a single sampling step and eliminates the need for training a teacher model. Our experiments on LJSpeech Dataset show that our ReFlow-TTS method achieves the best performance compared with other diffusion based models. And the ReFlow-TTS with one step sampling achieves competitive performance compared with existing one-step TTS models.Comment: Accepted at ICASSP202