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Background and Aim: Artificial intelligence (AI) is among the most promising innovations for transforming nursing education, making it more interactive, personalized, and competency-based. However, its integration also raises significant ethical and practical concerns. This scoping review aims to analyze and summarize key studies on the application of AI in university-level nursing education, focusing on its benefits, challenges, and future prospects. Methods: A scoping review was conducted using the Population, Concept, and Context (PCC) framework, targeting nursing students and educators in academic settings. A comprehensive search was carried out across the PubMed, Scopus, and Web of Science databases. Only peer-reviewed original studies published in English were included. Two researchers independently screened the studies, resolving any disagreements through team discussion. Data were synthesized narratively. Results: Of the 569 articles initially identified, 11 original studies met the inclusion criteria. The findings indicate that AI-based tools—such as virtual simulators and ChatGPT—can enhance students’ learning experiences, communication skills, and clinical preparedness. Nonetheless, several challenges were identified, including increased simulation-related anxiety, potential misuse, and ethical concerns related to data quality, privacy, and academic integrity. Conclusions: AI offers significant opportunities to enhance nursing education; however, its implementation must be approached with critical awareness and responsibility. It is essential that students develop both digital competencies and ethical sensitivity to fully leverage AI’s potential while ensuring high-quality education and responsible nursing practice.

Artificial Intelligence (AI) can play a powerful role in supporting climate action while boosting sustainable and inclusive economic growth. However, limited research exists on the potential influence of AI on the low-carbon transition. Here we identify five areas through which AI can help build an effective response to climate threats. We estimate the potential for greenhouse gas (GHG) emissions reductions through AI applications in three key sectors—power, food, and mobility—which collectively contribute nearly half of global emissions. This is compared with the increase in data centre-related emissions generated by all AI-related activities.

This paper investigates the relationship between energy demand, economic growth and prices in 24 non-OECD countries and three sectors from 1978-2003. We estimate linear and non-linear income andprice elasticities, using time fixed effects to control for unobserved dynamic effects such as technological change. We also test for asymmetric responses to price changes. The analysis leads to the following conclusions. First, the income elasticity of energy demand is high and increases with income, both on the country and the sector level. Second, energy demand is more responsive to end-use price than international oil price changes. Third, the price elasticity of energy demand increases with the price level. This result, driven by the residential and agricultural sector, is new to the literature for developing countries, and is consistent with the hypothesis of stronger responsiveness to high energy prices. Finally, we find that after including time fixed effects, allowing for price asymmetry adds little to the results.

The Global Commission on the Economy and Climate has shown that climate action could yield $26tn of direct economic benefits globally, create 65m new jobs in low-carbon industries and avoid 700,000 premature deaths from dangerous air pollution — all by 2030. Continued investment in fossil fuel infrastructure, particularly coal, does not make sound economic sense. The EBRD has not financed any greenfield coal-power generation facilities in well over five years, focusing instead on renewables, and the “no-coal, no caveats” policy has just become enshrined in our new energy sector strategy.

[...]countries that rely most heavily on fossil fuels are likely to face the highest risks of this transition, due either to their significant dependence on fossil fuel-related revenues or to their reliance on energy-intensive — and energy-emitting — industries. According to our simulations, a worldwide shift to a greener economy and the consequent decrease in demand for fossil fuels could lead to depletion of Kazakhstan’s national savings from oil as soon as a decade from now. Structural transformation that supports growth in the non-oil economy will reduce reliance on fossil fuels, building resilience to the fiscal risks of commodity price movements.

Three issues confront the global economy: massive government debt, a wide divergence between countries that save and those that consume, and the need for a lower-carbon economy. All three issues are linked, and must be dealt with together. Some solutions may reinforce each other; others may be at odds. The IMF is well placed to play a critical role, particularly through the following actions: 1. support governments in analyzing and designing policies to address market failings that can hinder the transition to a low-carbon economy, 2. support policies aimed at capturing the synergy between low-carbon investments and adjusting the global savings investment imbalance, 3. monitor and manage the risks associated with the tensions highlighted earlier, to ensure that policy objectives around the three challenges are pursued and advanced over the next decade, and 4. help generate alternative sources of finance for climate change to support countries in their low-carbon strategies without harming their ability to manage their public finances.

This paper revisits the contested issue of the impact of agricultural extension on farm productivity. Often studies in this .eld su¤er from a bias either due to self-selection of the best farmers for the extension services or to endogenous placement of the programme. The panel dataset collected by ANADER and the nature of the extension programme put into operation in Côte d.Ivoire between 1997 and 2001, allow to control for such biases and to deliver more robust estimates. The results indicate a positive impact of extension on yields, after controlling for other factors of production and for time and location e¤ects. While such e¤ect is signi.cant and of considerable magnitude for food crops, co¤ee and cocoa outputs seem to have behaved di¤erently. The results seem to suggests a tendency for farmers involved in extension to reduce their e¤orts in co¤ee and cocoa production, a .nding consistent with the recent experience in the country. Once we look at revenue the overall impact of extension disappears, indicating that the switch from cash to food crops, despite being the optimal choice during a period of deep crises for perennial crops in the international markets, did not increase the revenues of farmers.

This paper revisits the contested issue of the impact of agricultural extension on farm productivity. Often studies in this �eld suÃ,¤er from a bias either due to self-selection of the best farmers for the extension services or to endogenous placement of the programme. The panel dataset collected by ANADER and the nature of the extension programme put into operation in CÃ[function of]´te d�Ivoire between 1997 and 2001, allow to control for such biases and to deliver more robust estimates. The results indicate a positive impact of extension on yields, after controlling for other factors of production and for time and location eÃ,¤ects. While such eÃ,¤ect is signi�cant and of considerable magnitude for food crops, coffee and cocoa outputs seem to have behaved diÃ,¤erently. The results seem to suggests a tendency for farmers involved in extension to reduce their eÃ,¤orts in coÃ,¤ee and cocoa production, a �nding consistent with the recent experience in the country. Once we look at revenue the overall impact of extension disappears, indicating that the switch from cash to food crops, despite being the optimal choice during a period of deep crises for perennial crops in the international markets, did not increase the revenues of farmers.

Metastatic breast cancer cells disseminate to organs with a soft microenvironment. Whether and how the mechanical properties of the local tissue influence their response to treatment remains unclear. Here we found that a soft extracellular matrix empowers redox homeostasis. Cells cultured on a soft extracellular matrix display increased peri-mitochondrial F-actin, promoted by Spire1C and Arp2/3 nucleation factors, and increased DRP1- and MIEF1/2-dependent mitochondrial fission. Changes in mitochondrial dynamics lead to increased production of mitochondrial reactive oxygen species and activate the NRF2 antioxidant transcriptional response, including increased cystine uptake and glutathione metabolism. This retrograde response endows cells with resistance to oxidative stress and reactive oxygen species-dependent chemotherapy drugs. This is relevant in a mouse model of metastatic breast cancer cells dormant in the lung soft tissue, where inhibition of DRP1 and NRF2 restored cisplatin sensitivity and prevented disseminated cancer-cell awakening. We propose that targeting this mitochondrial dynamics- and redox-based mechanotransduction pathway could open avenues to prevent metastatic relapse. Romani et al. report that cells on soft extracellular matrix have increased mitochondrial fission, with subsequent production of mitochondrial reactive oxygen species and NRF2, which may increase resistance to reactive oxygen species-dependent chemotherapy drugs in breast cancer cells in vitro and in mouse lungs.