Urban bioeconomy: uncovering its components, impacts and the Urban Bio-Symbiosis

Rapid urbanisation has marked recent human history with more than 50% of the world's population now living in urban areas while the percentage and the overall population are still growing. Resource consumption is conglomerated in urban settlements which depend primarily on externally supplied and often fossil-based products. With the aim to transform cities sustainably from resource sinks to regenerative hubs, the concept of the Urban Bioeconomy poses a potential alternative to the current economic system. While the concept has been in discussion among practitioners and a few researchers, its meaning is not well established. Therefore, this study proposes a definition of the Urban Bioeconomy based on a systematic literature review, which focused on identifying its components, impacts and potential synergies within bioeconomic sectors, namely the ‘Urban Bio-Symbiosis’. This work highlights existing and emerging bioeconomic components within cities such as urban farming, biowaste valorisation methods and green infrastructure techniques. It outlines opportunities and challenges of the Urban Bioeconomy by presenting potential positive and negative environmental, economic, social and health impacts. Based on these results, it identifies the potential of the Urban Bio-Symbiosis and Resource Circularity as promising solutions to bring about synergistic effects between different urban bioeconomic components themselves and with the other parts of the economy. By proposing a definition of the Urban Bioeconomy, this work sets the ground for further research in this field

Numerical modeling and comparative analysis of electrolysis and electrodialysis systems for direct air capture

Electrochemical systems of alkali solution electrolysis and electrodialysis are prospective candidates for large-scale direct air capture (DAC) applications. In this study, electrolysis and electrodialysis DAC systems based on the pH-swing method are modeled and compared. The attainable membrane K+ selectivity and CO2 absorption ratio are modeled to accurately predict system performance. Considering the electrolysis system has dual functions for hydrogen production and DAC, its minimum energy demand for DAC is 1255 kJ mol-1 under a current density of 0.14 A cm-2, while the electrodialysis system has a minimum energy consumption of 1451 kJ mol-1 under a current density of 0.012 A cm-2. The effects of cycle solution flow rate and concentration are analyzed, and it is found that the solution pH difference between compartments needs to be greater than 5.25 to achieve optimum performance for both systems. Finally, the systems are simulated with 100% K+ selectivity and CO2 absorption ratio, and the results show that the lower limits on DAC energy demand for electrolysis and electrodialysis systems under such ideal conditions are reduced by around 90%, to 162 and 154 kJ mol-1, respectively. The improvement of K+ selectivity in electrochemical cells, and optimization of the air contactor to obtain higher CO2 absorption ratios are promising areas deserving further research

DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates

The enzyme turnover rate, kcat, quantifies enzyme kinetics by indicating the maximum efficiency of enzyme catalysis. Despite its importance, kcat values remain scarce in databases for most organisms, primarily because of the cost of experimental measurements. To predict kcat and account for its strong temperature dependence, DLTKcat was developed in this study and demonstrated superior performance (log10-scale root mean squared error = 0.88, R-squared = 0.66) than previously published models. Through two case studies, DLTKcat showed its ability to predict the effects of protein sequence mutations and temperature changes on kcat values. Although its quantitative accuracy is not high enough yet to model the responses of cellular metabolism to temperature changes, DLTKcat has the potential to eventually become a computational tool to describe the temperature dependence of biological systems

Decarbonisation pathways of the cement production process via hydrogen and oxy-combustion

Decarbonising cement production is of profound importance for meeting global greenhouse gas emission reduction targets and mitigating the impact of climate change. This study evaluates various technical options for achieving deep decarbonisation in a clinker production facility by utilising hydrogen (H2) as an alternative fuel to replace fossil fuels and by integrating an oxy-combustion technique with carbon capture and storage (CCS). Using Aspen Plus process simulations, we examined the extent of decarbonisation and assessed the thermal and electrical energy demands. This was achieved by incorporating an amine-absorption-based CO2 capture to a conventional natural gas fuelled reference plant, implementing oxyfuel-combustion of natural gas, and exploring four different scenarios for replacing fossil fuel with H2. In these scenarios, H2 was assumed to be produced through on-site water electrolysis, which also supplied oxygen for oxyfuel combustion, potentially eliminating the need for an air separation unit (ASU). The processes utilizing H2, except for the case of indirectly heated pre-calcination, employed oxyfuel combustion. The results indicate that the natural gas-fuelled oxyfuel-combustion process had the lowest total energy input at 4.92 GJ/t clinker, approximately 35% lower than that of the reference plant. Processes using H2 reduced energy demand by 11% in the H2-d scenario and 33% in the H2-a scenario. However, the process with indirect calcination required 6.24 GJ/t clinker, about 8% more H2 fuel than direct calcination but helped eliminate the need for an ASU. The results also reveal that greater H2 substitutions led to higher total process energy requirements due to the inefficiencies of the electrolysis process. While the H2-using processes could reduce the CO2 generation by up to 559 kgCO2/t clinker, this represents only about 27.6% of the CO2 reductions relative to the reference plant. These findings underscore the limitation of fuel substitution alone in cement production and emphasize the need for innovations in raw materials and the adoption of CCS to achieve deeper decarbonisation in cement industries

Reinforcement Learning in Computing and Network Convergence Orchestration

As computing power is becoming the core productivity of the digital economy era, the concept of Computing and Network Convergence (CNC), under which network and computing resources can be dynamically scheduled and allocated according to users' needs, has been proposed and attracted wide attention. Based on the tasks' properties, the network orchestration plane needs to flexibly deploy tasks to appropriate computing nodes and arrange paths to the computing nodes. This is a orchestration problem that involves resource scheduling and path arrangement. Since CNC is relatively new, in this paper, we review some researches and applications on CNC. Then, we design a CNC orchestration method using reinforcement learning (RL), which is the first attempt, that can flexibly allocate and schedule computing resources and network resources. Which aims at high profit and low latency. Meanwhile, we use multi-factors to determine the optimization objective so that the orchestration strategy is optimized in terms of total performance from different aspects, such as cost, profit, latency and system overload in our experiment. The experiments shows that the proposed RL-based method can achieve higher profit and lower latency than the greedy method, random selection and balanced-resource method. We demonstrate RL is suitable for CNC orchestration. This paper enlightens the RL application on CNC orchestration

Controlled Assembly of Sb₂S₃ Nanoparticles on Silica/Polymer Nanotubes:Insights into the Nature of Hybrid Interfaces

Silica nanotubes can serve as high aspect ratio templates for the deposition of inorganic nanoparticles to form novel hybrids. However, the nature of the interfacial binding is still an unresolved challenge when considered at the atomic level. In this work, novel nanocomposites have been successfully fabricated by the controlled nucleation and assembly of Sb(2)S(3) nanoparticles on the surface of mercaptopropyl-functionalized silica/polymer hybrid nanotubes (HNTs). The Sb(2)S(3) nanoparticles were strongly attached to the HNTs surface by interactions between the pendent thiol groups and inorganic sulfur atoms. Detailed analysis of the geometric and electronic structure using first–principle density functional theory demonstrates charge transfer from the nanoparticles to the underlying HNTs at the Sb(2)S(3)/HNTs interfaces. Formation of a packed array of Sb(2)S(3) nanoparticles on the HNTs results in mixing of the electronic states of the components, and is mediated by the mercaptopropyl bridges between Sb(2)S(3) and the outer layer of the HNTs

Systematic elucidation of independently modulated genes in Lactiplantibacillus plantarum reveals a trade-off between secondary and primary metabolism

Lactiplantibacillus plantarum is a probiotic bacterium widely used in food and health industries, but its gene regulatory information is limited in existing databases, which impedes the research of its physiology and its applications. To obtain a better understanding of the transcriptional regulatory network of L. plantarum, independent component analysis of its transcriptomes was used to derive 45 sets of independently modulated genes (iModulons). Those iModulons were annotated for associated transcription factors and functional pathways, and active iModulons in response to different growth conditions were identified and characterized in detail. Eventually, the analysis of iModulon activities reveals a trade-off between regulatory activities of secondary and primary metabolism in L. plantarum

Surrogate modelling-assisted comparison of reactor schemes for carbon dioxide removal by enhanced weathering of minerals using seawater

Reactor-based enhanced weathering of minerals represents one of the options for CO2 removal from the atmosphere to control the concentration of greenhouse gases for stabilising the climate. Earlier studies have modelled two reactor types, namely trickle-bed reactor and packed bubble column. However, their CO2 removal potential has not been compared. Building on the previous studies, this work further develops the mechanistic reactor models to enable them to consistently describe continuous weathering of minerals using seawater. Addressing the computational demands of the mechanistic models, a surrogate modelling-based optimisation procedure is developed to allow each reactor type to be rigorously optimised for minimising two competing objectives, namely energy consumption and space requirement. This has allowed the Pareto fronts of the two reactor types to be produced and compared. When applied to calcite weathering which is predominantly controlled by gas–liquid mass transfer, the packed-bubble column has been shown to consistently outperform the trickle-bed reactor, thanks to its superior mass transfer performance. However, when considering the weathering of forsterite, packed-bubble column performance is significantly worsened compared to calcite weathering, primarily because its low dissolution rate shifts the controlling mechanism of the process from gas/liquid mass transfer to solid dissolution. These results provide new insights to inform the future evaluation of reactor-based enhanced weathering schemes in terms of reactor design selection and the implication of mineral types

Novice Teacher Supports the Mathematics Learning of English Language Learners in Any Language

A novice teacher reflects on her first class of ELLs who spoke many native languages and shares her journey to improve. Three bang-for-the-buck strategies are shared. Support for her actions is provided through connections to the literature. Readers are encouraged to bravely move forward to use trial-and-error as they attempt to implement what they are learning from the many available resources which include anchor charts, sentence frames, manipulatives, and technology