The Interplay between Experimental Data and Uncertainty Analysis in Quantifying CO₂ Trapping during Geological Carbon Storage

Numerical simulation is a widely used tool for studying CO₂ storage in porous media. It enables the representation of trapping mechanisms and CO₂ retention capacity. The complexity of the involved physicochemical phenomena necessitates multiphase flow, accurate fluid and rock property representation, and their interactions. These include CO₂ solubility, diffusion, relative permeabilities, capillary pressure hysteresis, and mineralization, all crucial in CO₂ trapping during carbon storage simulations. Experimental data is essential to ensure accurate quantification. However, due to the extensive data required, modeling under uncertainty is often needed to assess parameter impacts on CO₂ trapping and its interaction with geological properties like porosity and permeability. This work proposes a framework combining laboratory data and stochastic parameter distribution to map uncertainty in CO₂ retention over time. Published data representing solubility, residual trapping, and mineral trapping are used to calibrate prediction models. Geological property variations, like porosity and permeability, are coupled to quantify uncertainty. Results from a saline sandstone aquifer model demonstrate significant variation in CO₂ trapping, ranging from 17% (P10 estimate) to 56% (P90), emphasizing the importance of considering uncertainty in CO₂ storage projects. Quadratic response surfaces and Monte Carlo simulations accurately capture this uncertainty, resulting in calibrated models with an R-squared coefficient above 80%. In summary, this work provides a practical and comprehensive framework for studying CO₂ retention in porous media, addressing uncertainty through stochastic parameter distributions, and highlighting its importance in CO₂ storage projects. 

A Position-based Hybrid Routing Protocol for Clustered Flying Ad Hoc Networks

Unmanned aerial vehicles (UAVs) have been used to establish flying ad hoc networks (FANETs) to support wireless communication in various scenarios, from disaster situations to wireless coverage extensions. However, the operation of FANETs faces mobility, wireless network variations and topology challenges. Conventional mobile ad hoc network and vehicular ad hoc network routing concepts have rarely been applied to FANETs, and even then they have produced unsatisfactory performance due to additional challenges not found in such networks. For instance, position-based routing protocols have been applied in FANET, but have failed to achieve adequate performance in large networks. Clustering solutions have also been used in large networks, but with a significant overhead in keeping track of the complete topology. Hence, to solve this problem, we propose a hybrid position-based segment-by-segment routing mechanism for clustered FANETs. This approach facilitates traffic engineering across multiple wireless clusters by combining position-based inter-cluster routing with a rank-based intra-cluster routing approach capable of balancing traffic loads between alternative cluster heads. Simulation results show that our solution achieves, on average, a lower power consumption of 72.5 J, a higher throughput of 275 Mbps and a much lower routing overhead of 17.5% when compared to other state-of-the-art end-to-end routing approaches.

Plant Proteins Availability in Europe and Asia: A Causality Analysis of Climate, Demographics, and Economic Factors

The article examines the availability of plant-based proteins in Europe and Asia, considering the challenges posed by climate, demographics, and economics. The availability of these proteins is crucial given the growing impact of climate, economic, and social variables. Indeed, these factors play a decisive role in the production and accessibility of plant-based proteins across countries. The study employed a causality analysis method using regression models to determine the relative impact of these factors on protein availability. Two indicators were prioritized: total national production and the daily accessible quantity per person. This approach made it possible to construct hypothetical trajectories, showcasing the interrelations between the different variables. The results show that the availability of plant-based proteins varies across regions. Factors such as rising temperatures, increasing pollutants, and rising prices of plant proteins are particularly concerning. In this context, legumes appear as a promising alternative. They offer resilience against climatic variations while being an excellent protein source. The findings also encourage rethinking our consumption. Meat, with its significant ecological footprint, should see its consumption decrease in favor of plant-based proteins, ensuring a more sustainable diet. To facilitate this transition, the importance of appropriate public policies and incentives for producing and consuming plant proteins is emphasized.

Climate Change Adaptation Strategies for Grape Cultivation in Yamanashi Prefecture of Japan

Climate change impacts agricultural production, especially fruits. Amongst fruits, the grape is economically valuable and highly affected by climate change. Therefore, climate adaptation strategies are essential in overcoming the detrimental effects of climate change on grape cultivation. The study summarises adaptation strategies for grape cultivation in general and focuses on climate change. The Yamanashi prefecture in Japan is taken for the case study. Our findings indicate a decline in grape production in Japan and Yamanashi prefecture. This is attributed to the effects of climate change. Following this, various support measures (adaptative, mitigation, others) provided by the Yamanashi government towards grape cultivation are summarised and analyzed. The study concludes by offering recommendations by drawing lessons from the literature review on adaptation strategies for grape cultivation, focusing on overcoming climate change impact in the context of Yamanashi prefecture.

The Future of Artificial Intelligence Will Be “Next to Normal”—A Perspective on Future Directions and the Psychology of AI Safety Concerns

This paper introduces the AI “next to normal”-thesis, suggesting that as Artificial Intelligence becomes more ingrained in our daily lives, it will transition from a sensationalized entity to a regular tool. However, this normalization has psychosocial implications, particularly when it comes to AI safety concerns. The “next to normal”-thesis proposes that AI will soon be perceived as a standard component of our technological interactions, with its sensationalized nature diminishing over time. As AI’s integration becomes more seamless, many users may not even recognize their interactions with AI systems. The paper delves into the psychology of AI safety concerns, discussing the “Mere Exposure Effect” and the “Black Box Effect”. While the former suggests that increased exposure to AI leads to a more positive perception, the latter highlights the unease stemming from not fully understanding its capabilities. These effects can be seen as two opposing forces shaping the public’s perception of the technology. The central claim of the thesis is that as AI progresses to become normal, human psychology will evolve alongside with it and safety concerns will diminish, which may have practical consequences. The paper concludes by discussing the implications of the “next to normal”-thesis and offers recommendations for the industry and policymakers, emphasizing the need for increased transparency, continuous education, robust regulation, and empirical research. The future of AI is envisioned as one that is seamlessly integrated into society, yet it is imperative to address the associated safety concerns proactively and not take the normalization effects take ahold of it.

Benzene Bridged Carbon Nitride for Efficient Photocatalytic Hydrogen Evolution

Turing the electronic structure by inserting certain functional groups in graphitic carbon nitride (g-C₃N₄, CN for short) skeleton through molecular doping is an effective way to improve its photocatalytic performance. Herein, we prepare a benzene bridged carbon nitride (BCN) by calcining urea and 1,3,5-tribromobenzene at elevated temperature. The introduction of benzene ring in g-C₃N₄ layers improves the separation efficiency and lifetime of photogenerated carriers, inhibits the recombination rate of electron/hole pairs, thus the performance of photocatalytic hydrogen evolution improves. The optimal hydrogen evolution rate of 1.5BCN reaches 1800 µmol/h·g, which is nine times that of the pure g-C₃N₄. DFT calculation proved the benzene bridged CN increased the distance of charge transfer (DCT) and the push-pull electronic effect of intramolecular electrons. This work may provide a pathway for preparing molecular doped g-C₃N₄ with improved photocatalytic performance.

Ecology and Diversity Editorial

A State-of-the-art Review on the Intelligent Tool Holders in Machining

In the manufacturing process, in addition to the properties of material itself, the quality of a product is directly related to the cutting process. Cutting force and cutting heat are two crucial factors in cutting processing. Researchers can analyze various signals during cutting process, such as cutting force signal, vibration signal, temperature signal, etc., which can regulate force and temperature, optimize the cutting process, and improve product quality. Therefore, it is very important to pay attention to various signals in cutting process. Meanwhile, good-quality signal data sets will greatly reduce time, resource and labor costs for subsequent use or analysis of researchers. Therefore, how to collect high-quality signals effectively and accurately is the first step. At present, researchers prefer to use various sensors to collect signals. With the advancement of science and technology, intelligent tool holder appears in researchers’ vision. It integrates multiple systems such as sensors, data collection, data transmission, and power supply on the tool holder. It replaces traditional wired sensors, and it is highly interactive with CNC machine tools. This paper will carry out a systematic review and prospect from three aspects: the structural design of the intelligent tool holder, the signal monitoring technology of the intelligent tool holder, and the tool condition monitoring of the intelligent tool holder.

A Perspective in Future Biomanufacturing: Challenges in Industrial Fermentation—Understanding and Controlling Microbial Lifespan and Aging

Deciphering the Code of Pattern Formation: Integrating In Silico and Wet Lab Approaches in Synthetic Biology

Pattern formation is a fundamental process in biological development, enabling the transformation of initially uniform or random states into spatially ordered structures. A comprehensive understanding of the formation and function of these patterns is crucial for unraveling the underlying principles of biological design and engineering. In recent years, synthetic biology has emerged as a powerful discipline for investigating and manipulating pattern formation in biological systems, involving the design and construction of novel biological components, circuits, and networks with specific functionalities. The integration of computational simulations (in silico) and experimental techniques (wet lab) in synthetic biology has significantly advanced our knowledge of pattern formation and its implications in biological design and engineering. This review provides an overview of the computational simulations employed in studying pattern formation and introduces the representative and cutting-edge experimental methods utilized in wet labs.