Frontier Environmental Technology (United States)

Abstract. High-precision and efficient atmospheric environmental forecasting is essential for protecting public health and supporting environmental management. However, traditional physics-based numerical models, while mechanistically interpretable, struggle to balance computational cost and forecast accuracy. Although artificial intelligence(AI) has advanced rapidly in meteorological forecasting, most existing AI models are not optimized for atmospheric environmental prediction and rely heavily on gridded inputs, limiting their ability to integrate site observations and their operational applicability. To overcome these limitations, we develop BiXiao, a new-generation AI-based atmospheric environmental forecasting model. BiXiao features a heterogeneous architecture with non-continuous grids, coupling independent meteorological and environmental modules for synergistic use of multi-source data. The meteorological module employs a 3D Swin Transformer(Swin3D) to process structured meteorological fields, while the environmental module directly assimilates discrete station data, enabling operational urban-scale forecasts. Testing in the Beijing-Tianjin-Hebei region shows that BiXiao completes 72-hour forecasts for six major pollutants across all key cities within 30 seconds. Compared with mainstream numerical models(CAMS and WRF-Chem), BiXiao achieves substantially higher computational efficiency and forecast accuracy, particularly during heavy pollution events.

Introduction To address the urgent need for enhanced grid flexibility in high-penetration distributed photovoltaic (PV) systems, this paper proposes a novel two-stage framework integrating multi-objective cluster partitioning and multi-agent reinforcement learning (MARL) for cooperative peak shaving. Methods First, a comprehensive multi-objective optimization model defines PV cluster partitioning using three domain-specific metrics: available peak shaving margin (APSM), peak shaving synergy (PSS), and node correlation degree (NCD). This approach replaces conventional clustering algorithms, ensuring partitions balance electrical compactness, regulation capacity, and operational coordination. Subsequently, each partitioned cluster acts as an autonomous agent in a hierarchical MARL framework. Leveraging the multi-agent deep deterministic policy gradient (MADDPG) algorithm, agents collaboratively optimize active/reactive power allocation among clusters while coordinating unit-level control within clusters. Results Validated on the IEEE-33 node system, the strategy achieves a 29.39% reduction in peak-to-valley difference and 54.44% improvement in power balance deviation, significantly smoothing net load curves and enhancing voltage stability. Discussion This work demonstrates how MARL-driven coordination unlocks the inherent flexibility of distributed PV clusters, providing a scalable solution for grid-edge resource participation in system-level peak shaving.

The UV filter benzophenone-3 (BP-3) is a prevalent environmental pollutant due to its extensive use in personal care products, raising concerns about its ecological risks. This study investigated the pH-dependent oxidative stress effects of benzophenone-3 (BP-3) on Carassius auratus by measuring four key biomarkers-superoxide dismutase, catalase, glutathione, and malonaldehyde in liver tissue. The results demonstrated that BP-3 exposure led to significant oxidative stress, as evidenced by altered antioxidant levels and increased lipid peroxidation. The integrated biomarker response indicated that stress was more pronounced at higher BP-3 concentrations and varied with pH, being greatest under acidic conditions. These findings demonstrate that the toxicity of BP-3 is strongly influenced by environmental pH, highlighting the necessity of incorporating pH variations into ecological risk assessments of ionizable organic pollutants.

We demonstrate a highly stackable and ultra-low leakage oxide-semiconductor channel transistor technology for 3D Oxide-semiconductor Channel Transistor DRAM (OCTRAM). It uses a replacement-type InGaZnO cell transistor with a novel Channel-Side WL (CSWL) configuration designed for Z-pitch scaling. This approach reduces process cost and improves multi-tier compatibility by utilizing mature Oxide/Nitride (O/N) stacks and deposited channels. The 3D OCTRAM architecture, integrating both vertical bit line (VBL) and word line (WL) select transistors with replacement-type designs, enables more WL stacks and continuous bit density growth. The prototype cell transistor demonstrates a high on-current of over 30µA/cell and an excellent on-off ratio >10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">13</sup> with a gate length (L<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</inf>) of 45 nm. An 8-layer stacked 3D OCTRAM cell transistor was successfully fabricated, confirming excellent transfer characteristics across all layers. We conclude that 3D OCTRAM is a promising candidate for future high-density, low-power 3D DRAM applications.

Tibial dyschondroplasia (TD) is a prevalent skeletal disorder in the modern broiler poultry industry. MicroRNAs (miRNAs) regulate various biological processes. Our previous studies suggest miR-1737 plays a role in cartilage development, but its mechanism in TD remains unclear. This study found that miR-1737 promotes chondrocyte proliferation and differentiation in TD chickens (P < 0.05), accelerating disease progression. Bioinformatics analysis predicted transforming growth factor β-activated kinase 1 (TAK1) as a target gene of miR-1737, which was confirmed through luciferase assays, qRT-PCR, and rescue experiments. TAK1 was shown to inhibit chondrocyte proliferation and differentiation (P < 0.05). Additionally, miR-1737 markedly upregulated the mRNA expression levels of Smad 1/5/8, key components of the BMP-Smad signaling pathway, and enhanced their phosphorylation, whereas TAK1 exhibited opposing effects. In conclusion, miR-1737 promotes TD chondrocyte proliferation and differentiation in vitro by targeting TAK1 and activating the BMP-Smad signaling pathway. These findings enhance our understanding of TD and suggest new molecular targets for its prevention and treatment.

To solve the problem of conflicting goals and disjointed cross domain optimization in the coordinated development of power marketing and comprehensive energy, this paper proposes a collaborative development model based on multiple swarm particle swarm optimization algorithms. This model constructs a closed-loop architecture of "data perception optimization decision-making execution feedback", integrating multi-dimensional data such as user electricity load, package preferences, combined cooling, heating, and power system output, wind and solar energy storage status, and achieving multi-objective optimization of comprehensive cost, energy utilization efficiency, and carbon emissions through a master-slave population collaborative particle swarm algorithm. We have researched and designed various engineering implementation schemes for swarm particle swarm optimization algorithms, clarified parameter configuration and operation flow, and verified the performance of the patterns through experiments. The results show that the proposed model improves the comprehensive cost reduction rate by 5.2 to 4.8 percentage points, energy utilization efficiency by 6.6 to 4.1 percentage points, and dynamic response delay by 65% to 34% compared to traditional particle swarm optimization algorithm and adaptive particle swarm optimization algorithm. It maintains linear scalability in large-scale user scenarios and effectively supports cross domain collaborative optimization of power marketing and comprehensive energy.

This study introduces a novel low-temperature electrochemical flow reactor for the efficient trichloromethylation of benzaldehyde using electrogenerated bases (EGBs). The setup achieved precise temperature control by leveraging a Peltier-cooled system and a divided flow reactor with carbon-felt electrodes without requiring external cooling baths. Optimization of reaction parameters, including flow rate, temperature, and charge passed, resulted in a maximum yield of 67 % for 2,2,2-trichloro-1-phenylethanol, demonstrating significantly enhanced stability and reactivity of EGBs. This system exhibits a productivity of 1.01 mmol h−1, which is 6.7 times higher than that of a prior microflow reactor approach, and successfully scales up to reactions involving 5 mmol of substrate. Cooling was identified as a critical factor in stabilizing the reactive intermediates, while further experiments confirmed the inefficacy of external cooling components alone. This robust and scalable reactor design highlights the potential for advancing low-temperature electrochemical synthesis and unlocking new reaction pathways.