Optimized water resource allocation is critical for promoting sustainable regional development. However, the intensification of climate change impacts has increased the variability of water availability, thereby compounding uncertainty and complexity in water allocation decision-making. Meanwhile, mismatches in the spatial distribution of water supply and demand have also become an essential constraint to effectiveness and fairness of water allocation. Prior studies show weak correspondence between modeling frameworks to real world conditions and insufficiently describe regional balance, as well as the dynamic interaction between water inflow and demand under varying hydrological regimes. To address these challenges, this study develops a multi-objective water resource allocation model guided by spatial equilibrium principles, ensuring not only fair inter-regional allocation but also balanced co-development among water, socio-economic, and ecological subsystems. Regional balance is quantified with the Gini coefficient. Subsystem co-development is enforced via the coupling coordination degree. To address uncertainty, a nested multi-scenario robust optimization framework is proposed. It applies copula function to generate realistic encounter scenarios and applies a multi-objective probabilistic robustness evaluation to test solution stability and adaptability across scenarios. Applied to the upper and middle reaches of the Huaihe River Basin (HRB), the approach reduces the water-deficit rate by 45.42 % under extreme dry scenarios, lifts the coordination index to > 0.75, and increases reliability by 45.3 % compared with a traditional robust optimization baseline, demonstrating effectively optimize complex water resource systems while keeping both fairness and stability. This research offers a theoretical and practical framework for optimizing water resource allocation in complex and uncertain environments, contributing to the advancement of resilient and equitable water governance.

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial–temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techniques, physical modeling, and model complexity, thereby providing valuable insights for further developments in this field.

Nicotinamide adenine dinucleotide (NAD) and its 2′-phosphorylated form NADP are crucial cofactors for a large array of biocatalytically important redox enzymes. Their high cost and relatively poor stability, however, make them less attractive electron mediators for industrial processes. Nicotinamide cofactor biomimetics (NCBs) are easily synthesized, are inexpensive, and are also generally more stable than their natural counterparts. A bottleneck for the application of these artificial hydride carriers is the lack of efficient cofactor recycling methods. Therefore, we engineered the thermostable F420:NADPH oxidoreductase from Thermobifida fusca (Tfu-FNO), by structure-inspired site-directed mutagenesis, to accommodate the unnatural N1 substituents of eight NCBs. The extraordinarily low redox potential of the natural cofactor F420H2 was then exploited to reduce these NCBs. Wild-type enzyme had detectable activity toward all selected NCBs, with Km values in the millimolar range and kcat values ranging from 0.09 to 1.4 min-1. Saturation mutagenesis at positions Gly-29 and Pro-89 resulted in mutants with up to 139 times higher catalytic efficiencies. Mutant G29W showed a kcat value of 4.2 s-1 toward 1-benzyl-3-acetylpyridine (BAP+), which is similar to the kcat value for the natural substrate NADP+. The best Tfu-FNO variants for a specific NCB were then used for the recycling of catalytic amounts of these nicotinamides in conversion experiments with the thermostable ene-reductase from Thermus scotoductus (TsOYE). We were able to fully convert 10 mM ketoisophorone with BAP+ within 16 h, using F420 or its artificial biomimetic FOP (FO-2′-phosphate) as an efficient electron mediator and glucose-6-phosphate as an electron donor. The generated toolbox of thermostable and NCB-dependent Tfu-FNO variants offers powerful cofactor regeneration biocatalysts for the reduction of several artificial nicotinamide biomimetics at both ambient and high temperatures. In fact, to our knowledge, this enzymatic method seems to be the best-performing NCB-recycling system for BNAH and BAPH thus far.

Bubbly turbulent flow in a channel is investigated using interface-resolved direct numerical simulation. An efficient coupled level-set volume-of-fluid solver based on a fast Fourier transform algorithm is implemented to enable a high resolution and fast computation at the same time. Up to 384 bubbles are seeded in the turbulent channel flow corresponding to 5.4% gas volume fraction. Bubbles are clustered in the channel center due to the downward flow direction. The bubbles induce additional pseudo-turbulence in the channel center and are also able to attenuate the energy in the boundary layer by reducing the shear production. Turbulent kinetic energy budget indicates a significant buoyancy production in the channel center. A local equilibrium between buoyancy production and dissipation is observed here besides the shear production peak in the boundary layer. Comparing the local production and dissipation indicates a coexistence of boundary layer turbulence near the wall and bubble-induced pseudo-turbulence in the channel center. The liquid phase and gas phase are coupled through the complex liquid-gas interface. Local flow topology analysis is depicted in the liquid phase around the bubbles as well as in the gas phase. The flow topology of the liquid phase and the gas phase differs from each other significantly. Local dissipation is more dominant in the liquid phase near the bubble interface, whereas local enstrophy is preferred in the gas phase. In the liquid phase, a high dissipation event is preferred close to the interface, whereas a high enstrophy event is dominant away from the interface.