Wastewater-integrated microalgal bioprocesses have emerged as a promising approach for simultaneous wastewater treatment, resource recovery and valuable biomass generation within a circular bioeconomy. Nevertheless, the presence of pathogens in wastewater poses critical challenges that compromise cultivation performance, biomass quality, safety, process stability and downstream applications. Accordingly, this review presents a critical perspective on pathogen reduction as an essential upstream process for the design and operation of wastewater-integrated microalgal bioprocesses. Moreover, physical, chemical and electrochemical pathogen-reduction technologies are critically assessed in terms of sterilization/disinfection efficiency, nutrient retention and bioavailability, impacts on microalgal growth, operational requirements, process integration potential, techno-economic feasibility, and environmental sustainability. Conventional sterilization techniques, while effective for pathogen reduction, often reduce the economic and environmental feasibility of wastewater-integrated microalgae cultivation systems due to high energy demand, nutrient losses and additional processing steps. In contrast, solar-driven ultraviolet irradiation and other low-energy pretreatment strategies demonstrate significant potential to enhance process sustainability, particularly in high-irradiance regions. Additionally, the review discusses process engineering strategies, including multi-criteria decision-making for technology selection, mathematical modelling, process simulation, optimization, advanced monitoring and control, and techno-economic and environmental sustainability assessments, for designing scalable pathogen reduction technologies. Furthermore, key knowledge gaps, engineering challenges and future perspectives are identified to facilitate the industrial implementation of safe, economically viable and resource-efficient wastewater-integrated microalgal bioprocesses within a circular bioeconomy framework.
Microalgae are a promising renewable fuel feedstock due to their high carbon fixation potential and favorable heating value; however, their extremely high moisture content makes drying energy-intensive and a major barrier to large-scale utilization. This study proposes a waste-assisted drying strategy that simultaneously addresses energy consumption, fuel quality degradation, and material circularity. Discarded air-filter residues, consisting of hygroscopic nonwoven fibers and thermally conductive activated carbon, are repurposed to enable rapid convective drying of concentrated microalgae slurry at low temperature (60 degrees C). The binary effects of rapid moisture absorption and enhanced heat transfer accelerate water removal while suppressing oxidation reactions that typically degrade fuel properties during thermal drying. Experimental results show that the proposed method achieves a water activity (dried microalgae) of 0.58, effectively inhibiting microbial growth, while reducing oxide formation by approximately 5.5 times compared with conventional convective drying. Drying kinetics reveal a forward shift in the critical moisture content and the emergence of a second falling-rate period, indicating a distinct moisture-transport mechanism induced by the filter-residue layers. Life cycle assessment further demonstrates that the developed drying approach lowers greenhouse gas emissions by 4.5 kg CO2-eq per kilogram of dried microalgae relative to freeze-drying, with additional emission reductions achieved when drying is performed prior to transportation. Overall, this work demonstrates a low-carbon, fuel-oriented drying pathway that integrates waste valorization with improved energy performance, supporting the sustainable deployment of microalgae as a solid biofuel feedstock.
Simulation-based data synthesis has emerged as a powerful paradigm for enhancing real-world robotic manipulation. However, existing synthetic datasets remain insufficient for robust bimanual manipulation due to two key challenges: (1) the lack of an autonomous self-correcting mechanism to resolve execution failures in complex coordination tasks, and (2) the scarcity of diverse visual and spatial variations required to bridge the sim-to-real gap. To this end, we present RoboTwin 2.0, a scalable simulation framework that enables closed-loop, automated, large-scale generation of diverse and realistic data, along with unified evaluation protocols for dual-arm manipulation. Built upon RoboTwin-OD, a foundational library of 731 instances across 147 categories with rich semantic annotations, our framework integrates Multimodal Large Language Models (MLLMs) with simulation-in-the-loop verification. This integration forms an automated feedback mechanism that significantly boosts the success rate of expert task program generation. To enhance robust sim-to-real transfer, RoboTwin 2.0 incorporates structured domain randomization along five axes: clutter, lighting, background, tabletop height and language instructions, thereby maximizing data diversity. We instantiate this framework across 50 dual-arm tasks spanning five robot embodiments. Empirical evaluations demonstrate that Vision-Language-Action (VLA) models pre-trained on our synthetic data achieve a 3.6x improvement in few-shot real-world transfer (over a 10-demo baseline) and a 2.2x gain in zero-shot generalization. We release the data generator, benchmark, pre-collected dataset, and code to support scalable research in robust bimanual manipulation.
We present LongLive, a frame-level autoregressive (AR) framework for real-time and interactive long video generation. Long video generation presents challenges in both efficiency and quality. Diffusion and Diffusion-Forcing models can produce high-quality videos but suffer from low efficiency due to bidirectional attention. Causal attention AR models support KV caching for faster inference but often degrade in quality on long videos due to memory challenges during long-video training. In addition, beyond static prompt-based generation, interactive capabilities, such as streaming prompt inputs, are critical for dynamic content creation, enabling users to guide narratives in real time. This interactive requirement significantly increases the complexity, especially in ensuring visual consistency and semantic coherence during prompt transitions. To address these challenges, LongLive adopts a causal, frame-level AR design that integrates a KV-recache mechanism that refreshes cached states with the new prompt for smooth, adherent switches streaming long tuning to enable long video training and to align training and inference (train-long–test-long); and short window attention paired with a frame-level attention sink, preserving long-range consistency while enabling faster generation. With these key designs, LongLive fine-tunes a 1.3B-parameter short-clip model to minute-long generation in just 32 GPU-days. At inference, LongLive sustains 20.7 FPS on a single NVIDIA H100, achieves strong performance on VBench in both short- and long-video settings. LongLive supports up to 240-second videos on a single H100 GPU. With FP8 quantization, LongLive boosts inference to 24.8 FPS with marginal quality loss.
