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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. 

Radiance fields have gained tremendous success with applications ranging from novel view synthesis to geometry reconstruction, especially with the advent of Gaussian splatting. However, they sacrifice modeling of material reflective properties and lighting conditions, leading to significant geometric ambiguities and the inability to easily perform relighting. One way to address these limitations is to incorporate physically-based rendering, but it has been prohibitively expensive to include full global illumination within the inner loop of the optimization. Therefore, previous works adopt simplifications that make the whole optimization with global illumination effects efficient but less accurate. In this work, we adopt Gaussian surfels as the primitives and build an efficient framework for differentiable light transport, inspired from the classic radiosity theory. The whole framework operates in the coefficient space of spherical harmonics, enabling both diffuse and specular materials. We extend the classic radiosity into non-binary visibility and semi-opaque primitives, propose novel solvers to efficiently solve the light transport, and derive the backward pass for gradient optimizations, which is more efficient than auto-differentiation. During inference, we achieveview-independentrendering where light transport need not be recomputed under viewpoint changes, enabling hundreds of FPS for global illumination effects, including view-dependent reflections using a spherical harmonics representation. Through extensive qualitative and quantitative experiments, we demonstrate superior geometry reconstruction, view synthesis and relighting than previous inverse rendering baselines, or data-driven baselines given relatively sparse datasets with known or unknown lighting conditions. 

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. 

Proteinuria, the presence of high molecular weight proteins in the urine, is a primary indicator of chronic kidney disease. Proteinuria results from increased molecular permeability of the glomerular filtration barrier combined with saturation or defects in tubular protein reabsorption. Any solute that passes into the glomerular filtrate traverses the glomerular endothelium, the glomerular basement membrane, and the podocyte slit diaphragm. Damage to any layer of the filter has reciprocal effects on other layers to increase glomerular permeability. The GBM is thought to act as a compressible ultrafilter that has increased molecular selectivity with increased pressure due to compression that reduced the porosity of the GBM with increased pressure. In multiple forms of chronic kidney disease, crosslinking enzymes are upregulated and may act to increase GBM stiffness. Here we show that enzymatically crosslinking porcine GBM with transglutaminase increases the stiffness of the GBM and mitigates pressure-dependent reductions in molecular sieving coefficient. This was modeled mathematically using a modified membrane transport model accounting for GBM compression. Changes in the mechanical properties of the GBM may contribute to proteinuria through pressure-dependent effects on GBM porosity. 

Carbohydrate-derived molecular gelators have found many practical applications as soft materials. To better understand the structure and molecular gelation relationship and further explore the applications of sugar-based gelators, we designed and synthesized eight trimeric branched sugar triazole derivatives and studied their self-assembling properties. These included glucose, glucosamine, galactose, and maltose derivatives. Interestingly, the gelation properties of these compounds exhibited correlations with the peripheral sugar structures. The maltose derivative did not form gels in the tested solvents, but all other compounds exhibited gelation properties in at least one of the solvents. Glucose derivatives showed superior performance, followed by glucosamine derivatives. They typically formed gels in toluene and alcohols; some formed gels in ethanol-water mixtures or DMSO water mixtures. The glycoclusters 9 and 10 demonstrated rate acceleration for the copper-catalyzed azide-alkyne cycloaddition (CuAAC) reactions. These were further studied for their metallogels formation properties, and the copper metallogels from compound 9 were successfully utilized to catalyze click reactions. These metallogels were able to form a gel column, which was effective in converting the reactants into the triazole products in multiple cycles. Moreover, the same gel column was used to transform a second click reaction using different reactants. The synthesis and characterization of these compounds and their applications for catalytic reactions were discussed. 

Abstract While powder bed fusion (PBF) additive manufacturing offers many advantages and exciting applications, its broader adoption is hindered by issues with reliability and variations during the manufacturing process. To address this, researchers have identified the importance of using both finite element modeling and control-oriented modeling to predict and improve the quality of printed parts. In this paper, we propose a novel control-oriented multi-track melt pool width model that utilizes the superposition principle to account for the complex thermal interactions that occur during PBF. We validate the effectiveness of the model by applying a finite element model of the thermal fields in PBF.