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The rapid rise of AI applications has driven datacenters to unprecedented energy demands, which has prompted major tech companies to adopt on-site nuclear power plants (NPPs) alongside grid electricity. While existing research focuses on off-site NPPs in multi-energy systems optimized for investment returns, recent advances in small modular reactors (SMRs), particularly load-following SMRs (LF-SMRs), offer flexible, reliable power tailored for datacenter co-location. However, LF-SMRs are governed by a set of physical constraints, such as ramp rate and stability limits, making them unsuitable as fully dispatchable sources. This paper proposes a novel day-ahead workload scheduling approach that jointly coordinates datacenter operations and LF-SMR output, explicitly modeling these constraints. We develop a two-stage formulation that forecasts carbon-free grid energy from the grid using conformal prediction in the first stage and then optimizes LF-SMR output and workload scheduling via mixed-integer programming in the second stage. Evaluation on real workload traces shows that our method reduces carbon-based energy consumption by up to 43.44% compared to baselines that omit nuclear integration or ignore SMR limitations. 

We present theoretical observations on the topological nature of strained III–V semiconductors. By k·p perturbation, it can be shown that the strain-engineered conduction band hosts a Kramers–Weyl node at the Γ point. It is theoretically shown that a curated strain can create and then tune the sign of the topological charge. Furthermore, we outline experimental methods for both the realization and detection of strain-induced topological phase transitions. 

Abstract Multi-year marine heatwaves (MHWs) in the Gulf of Alaska (GOA) are major climate events with lasting ecological and economic effects. Though often seen as local Pacific phenomena, our study shows their persistence depends on trans-basin interactions between the North Pacific and North Atlantic. Using observational data and climate model experiments, we find that prolonged MHWs occur as sequential warming episodes triggered by atmospheric wave trains crossing ocean basins. These wave trains alter surface heat flux, initiating MHWs in the GOA and changing North Atlantic sea surface temperatures (SSTs). In turn, Atlantic SST anomalies reinforce wave activity, fueling subsequent MHW episodes in a feedback loop. This mechanism appears in historical events (1949–52, 1962–65, 2013–16, and 2018–22), highlighting MHWs as a trans-basin phenomenon. Our findings link GOA MHWs to trans-basin atmospheric wave dynamics and identify North Atlantic SSTs as a potential predictor of their duration. 

Buildings produce a significant share of greenhouse gas (GHG) emissions, making homes and businesses a major factor in climate change. To address this critical challenge, this paper explores achieving net-zero emission through the carbon-aware optimal scheduling of the multi-energy building integrated energy systems (BIES). We integrate advanced technologies and strategies, such as the carbon capture system (CCS), power-to-gas (P2G), carbon tracking, and emission allowance trading, into the traditional BIES scheduling problem. The proposed model enables accurate accounting of carbon emissions associated with building energy systems and facilitates the implementation of low-carbon operations. Furthermore, to address the challenge of accurately assessing uncertainty sets related to forecasting errors of loads, generation, and carbon intensity, we develop a learning-based robust optimization approach for BIES that is robust in the presence of uncertainty and guarantees statistical feasibility. The proposed approach comprises a shape learning stage and a shape calibration stage to generate an optimal uncertainty set that ensures favorable results from a statistical perspective. Numerical studies conducted based on both synthetic and real-world datasets have demonstrated that the approach yields up to 8.2% cost reduction, compared with conventional methods, in assisting buildings to robustly reach net-zero emissions. 

Abstract Based on a template-matching method, we estimate the barium (Ba) abundances for stellar spectra from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Medium-Resolution Spectroscopic Survey (MRS). The Ba abundances of 198,011 stars have been determined from MRS spectra with signal-to-noise ratios (S/N) > 40 combined with the stellar atmospheric parameters from the LAMOST Low-Resolution Spectroscopic Survey DR9 by the LAMOST Stellar Parameter Pipeline. The uncertainties in the Ba abundances from the LAMOST MRS spectra are less than 0.3 dex when S/N exceeds 40, which align closely with the results based on the high-resolution UVES spectra from the Gaia-ESO survey obtained by spectral synthesis. Further analysis of Ba abundances from repeated observations of the same stars reveals that random errors related to spectral quality remain below 0.3 dex at the same S/N, with a systematic overestimation for the low-S/N spectra. This extensive sample of stellar Ba abundances will enhance studies of thes-,i-, andr-processes, and deepen our understanding of the chemical-evolution history of the Milky Way.