A framework to predict the price of energy for the end-users with applications to monetary and energy policies

Potential capture and conversion of CO2 from oceanwater through mineral carbonation

Carbon dioxide (CO₂) emitted by human activities not only brings about a serious greenhouse effect but also accelerates global climate change. This has resulted in extreme climate hazards that can obstruct human development in the near future. Hence, there is an urgent need to achieve carbon neutrality by increasing negative emissions. The ocean plays a vital role in absorbing and sequestering CO₂. Current research on marine carbon storage and sink enhancement mainly focuses on biological carbon sequestration using carbon sinks (macroalgae, shellfish, and fisheries). However, seawater inorganic carbon accounts for more than 95 % of the total carbon in marine carbon storage. Increasing total alkalinity at a constant dissolved inorganic carbon shifts the balance of existing seawater carbonate system and prompts a greater absorption of atmospheric CO₂, thereby increasing the ocean's "carbon sink". This review explores two main mechanisms (i.e., enhanced weathering and ocean alkalinization) and materials (e.g., silicate rocks, metal oxides, and metal hydroxides) that regulate marine chemical carbon sink (MCCS). This work also compares MCCS with other terrestrial and marine carbon sinks and discusses the implementation of MCCS, including the following aspects: chemical reaction rate, cost, and possible ecological and environmental impacts.

Metal-Based Nanozymes with Multienzyme-Like Activities as Therapeutic Candidates: Applications, Mechanisms, and Optimization Strategy

Most nanozymes in development for medical applications only exhibit single-enzyme-like activity, and are thus limited by insufficient catalytic activity and dysfunctionality in complex pathological microenvironments. To overcome the impediments of limited substrate availabilities and concentrations, some metal-based nanozymes may mimic two or more activities of natural enzymes to catalyze cascade reactions or to catalyze multiple substrates simultaneously, thereby amplifying catalysis. Metal-based nanozymes with multienzyme-like activities (MNMs) may adapt to dissimilar catalytic conditions to exert different enzyme-like effects. These multienzyme-like activities can synergize to realize "self-provision of the substrate," in which upstream catalysts produce substrates for downstream catalytic reactions to overcome the limitation of insufficient substrates in the microenvironment. Consequently, MNMs exert more potent antitumor, antibacterial, and anti-inflammatory effects in preclinical models. This review summarizes the cellular effects and underlying mechanisms of MNMs. Their potential medical utility and optimization strategy from the perspective of clinical requirements are also discussed, with the aim to provide a theoretical reference for the design, development, and therapeutic application of their catalytic effects.

A systems engineering framework for the optimization of food supply chains under circular economy considerations

The current linear "take-make-waste-extractive" model leads to the depletion of natural resources and environmental degradation. Circular Economy (CE) aims to address these impacts by building supply chains that are restorative, regenerative, and environmentally benign. This can be achieved through the re-utilization of products and materials, the extensive usage of renewable energy sources, and ultimately by closing any open material loops. Such a transition towards environmental, economic and social advancements requires analytical tools for quantitative evaluation of the alternative pathways. Here, we present a novel CE system engineering framework and decision-making tool for the modeling and optimization of food supply chains. First, the alternative pathways for the production of the desired product and the valorization of wastes and by-products are identified. Then, a Resource-Task-Network representation that captures all these pathways is utilized, based on which a mixed-integer linear programming model is developed. This approach allows the holistic modeling and optimization of the entire food supply chain, taking into account any of its special characteristics, potential constraints as well as different objectives. Considering that typically CE introduces multiple, often conflicting objectives, we deploy here a multi-objective optimization strategy for trade-off analysis. A representative case study for the supply chain of coffee is discussed, illustrating the steps and the applicability of the framework. Single and multi-objective optimization formulations under five different coffee-product demand scenarios are presented. The production of instant coffee as the only final product is shown to be the least energy and environmental efficient scenario. On the contrary, the production solely of whole beans sets a hypothetical upper bound on the optimal energy and environmental utilization. In both problems presented, the amount of energy generated is significant due to the utilization of waste generated for the production of excess energy.

Surface Design for Antibacterial Materials: From Fundamentals to Advanced Strategies

Healthcare-acquired infections as well as increasing antimicrobial resistance have become an urgent global challenge, thus smart alternative solutions are needed to tackle bacterial infections. Antibacterial materials in biomedical applications and hospital hygiene have attracted great interest, in particular, the emergence of surface design strategies offer an effective alternative to antibiotics, thereby preventing the possible development of bacterial resistance. In this review, recent progress on advanced surface modifications to prevent bacterial infections are addressed comprehensively, starting with the key factors against bacterial adhesion, followed by varying strategies that can inhibit biofilm formation effectively. Furthermore, "super antibacterial systems" through pre-treatment defense and targeted bactericidal system, are proposed with increasing evidence of clinical potential. Finally, the advantages and future challenges of surface strategies to resist healthcare-associated infections are discussed, with promising prospects of developing novel antimicrobial materials.

Pay Attention to Evolution: Time Series Forecasting with Deep Graph-Evolution Learning

Time-series forecasting is one of the most active research topics in artificial intelligence. A still open gap in that literature is that statistical and ensemble learning approaches systematically present lower predictive performance than deep learning methods. They generally disregard the data sequence aspect entangled with multivariate data represented in more than one time series. Conversely, this work presents a novel neural network architecture for time-series forecasting that combines the power of graph evolution with deep recurrent learning on distinct data distributions; we named our method Recurrent Graph Evolution Neural Network (ReGENN). The idea is to infer multiple multivariate relationships between co-occurring time-series by assuming that the temporal data depends not only on inner variables and intra-temporal relationships (i.e., observations from itself) but also on outer variables and inter-temporal relationships (i.e., observations from other-selves). An extensive set of experiments was conducted comparing ReGENN with dozens of ensemble methods and classical statistical ones, showing sound improvement of up to 64.87% over the competing algorithms. Furthermore, we present an analysis of the intermediate weights arising from ReGENN, showing that by looking at inter and intra-temporal relationships simultaneously, time-series forecasting is majorly improved if paying attention to how multiple multivariate data synchronously evolve.