Browse Articles

Dynamic volume compensation realizing Ah-level all-solid-state silicon-sulfur batteries

Microfluidic nanobubbles produced using a micromixer for ultrasound imaging and gene delivery

Observation of sub-relativistic collisionless shock generation and breakout dynamics

Automatic smart brain tumor classification and prediction system using deep learning

An anti-virulence drug targeting the evolvability protein Mfd protects against infections with antimicrobial resistant ESKAPE pathogens

Multi-objective quantum hybrid evolutionary algorithms for enhancing quality-of-service in internet of things

Unveiling fine-scale spatial structures and amplifying gene expression signals in ultra-large ST slices with HERGAST

Multi-UAV path planning considering multiple energy consumptions via an improved bee foraging learning particle swarm optimization algorithm

Exploring techno-economic landscapes of abatement options for hard-to-electrify sectors

Abstract Approximately 20% of global CO 2 emissions originate from sectors often labeled as hard-to-abate, which are challenging or impossible to electrify. Alternative abatement options are necessary for these sectors but face critical bottlenecks, particularly concerning the availability and cost of low-emission hydrogen, carbon capture and storage, and non-fossil CO 2 for synthetic fuels or carbon-dioxide removal. In this study, we conduct a broad techno-economic analysis, mapping abatement options and hard-to-electrify sectors while addressing associated technological uncertainties. Our findings reveal a diverse mitigation landscape that can be categorized into three tiers, based on the abatement cost and technologies required. By requiring long-term climate neutrality through simple conditions, the mitigation landscape narrows substantially, with single options dominating each sector. This clarity justifies targeted political support for sector-specific abatement options, increasing investment security for transforming hard-to-electrify sectors.

Theoretical and simulation analysis of a rectangular crack in the piezoelectric material

Dissecting cross-population polygenic heterogeneity across respiratory and cardiometabolic diseases

Computational intelligence modeling and optimization of small molecule API solubility in supercritical solvent for production of drug nanoparticles

Association between lean body mass and osteoarthritis: a cross-sectional study from the NHANES 2007–2018

The dynamics of leadership and success in software development teams

Epidemiology and predictors of suicide and suicide attempt in Northwest Iran: a pilot study for local prevention strategies

Probabilistic alignment of multiple networks

Study on the preparation of sterile noble metal nanoparticles and hydrotalcite layered nanoparticles by innovative high pressure sterilization method

Enhanced heating effect of lakes under global warming

Leveraging multi-source data and teleconnection indices for enhanced runoff prediction using coupled deep learning models

Biotic resistance predictably shifts microbial invasion regimes

Abstract Invading new territory is a central aspect of the microbial lifestyle. However, invading microbes rarely find novel territories uninhabited; resident microbes can interact with the newcomers and, in many cases, impede their invasion – an effect known as ‘biotic resistance’. Accordingly, invasions are shaped by the interplay between dispersal and resistance. However, these two factors are difficult to disentangle or manipulate in natural systems, making their interplay challenging to understand. To address this challenge, we track microbial invasions in the lab over space and time – first in a model system of two interacting microbes, then in a multi-strain system involving a pathogen invading resident communities. In the presence of biotic resistance, we observe three qualitatively different invasion regimes: ‘consistent’, ‘pulsed’, and ‘pinned’, where, in the third regime, strong biotic resistance stalls the invasion entirely despite ongoing invader dispersal. These rich invasion dynamics could be qualitatively predicted with a simple, parameter-free framework that ignores individual species interactions, even for rather complex communities. Moreover, we show that this simple framework could accurately predict simulated invasions from different mechanistic models, indicating its broad applicability. Our work offers an understanding of how biotic resistance impacts invasions and introduces a predictive tool to identify invasion-resistant communities.