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This article aims to highlight the importance of climate and environmental challenges for agricultural economics and policy. Empirical research based on the Data Envelopment Analysis (DEA) method determined the average technical efficiency and scale efficiency of farms in the European Union in total and in economic size classes in the period 2004-2020. The results indicate that agriculture is generally characterised by high technical efficiency and scale efficiency. Analysis by economic size classes of farms, defined by the standard sum of their agricultural output, shows that the relationship between the scale of production and technical efficiency of farms is U-shaped. The highest technical efficiency and scale efficiency are characterised by small, large and very large farms economic classes. Economies of scale shift the burden of food production to large farms, which provide food security and are technically efficient but excessively burdensome for the climate and the environment. Small farms produce environmentally friendly food but on a small scale. Therefore, increasing the technical efficiency of medium-sized farms can contribute to more sustainable food production that meets both food security and climate and environmental objectives. The Common Agricultural Policy 2023-2027 provides greater access to financial support for moderate-scale farms and farms undertaking ecosystem restoration activities. This may affect the strength and direction of the relationship between farm scale and productivity, including technical efficiency.

Solving the problem of agricultural water use efficiency is an effective means to understand the agricultural ecological civilization and food security. In order to balance the relationship between agricultural water efficiency and regional economic size, this study examines the agricultural water use efficiency in China based on the Super-SBM model of unexpected output. At the same time, the Spatial Durbin Model (SDM) was introduced to analyze the spatial spillover effect and try to find ways to improve agricultural water use efficiency from the perspective of influencing factors. The present study used the panel data of 30 provinces in China from 1998 to 2018 to obtain empirical results. The results show that (1) China's agricultural water resources’ utilization efficiency in 21 years has not remained high: the results showed that it was 0.496 in 1998, 0.572 in 2008, and 0.657 in 2018, but it is slowly rising which explains that the low efficiency is mainly caused by the low pure technical efficiency. (2) The overall agricultural water use efficiency in China is in a situation of spatial agglomeration but there has been a trend of shifting positive correlations to negative correlations among neighboring provinces. It found that in 1998, the sum of the number of `High-high agglomeration type' and `low-low agglomeration type' provinces was 20, and the sum of `low-high agglomeration type' and `high-low agglomeration type' provinces was 10. In 2018, the total number of `high-high agglomeration type' and `low-low agglomeration type' provinces decreased to 12, while the total number of `low-high agglomeration type' and `high-low agglomeration type' provinces increased to 18, which specifically manifested as a negative spillover effect. (3) During the research period, the province's agricultural fixed asset investment, fiscal expenditure on agriculture, forestry, and water affairs, the number of years of education of rural residents and the increase in crop sown area have positive impacts on the efficiency of agricultural water use in the province. Meanwhile the transfer of labor and increase in the disposable income of rural residents reduced the efficiency of agricultural water use in the province. Fixed agricultural investment, labor transfers, and financial expenditures for agriculture, forestry, and water affairs in neighboring provinces have negative impacts on the province's agricultural water use efficiency. The impact is specifically manifested as a negative spatial spillover effect. As a result, China's agriculture will move towards healthy and green development. Therefore, the efficiency of agricultural water use needs to be comprehensively improved. HIGHLIGHTS Agricultural water use efficiency and the spatial spillover effect.; Taking agricultural carbon emissions as an unexpected output.; The study used unexpected output Super-SBM model and SDM.; Agricultural water use efficiency among provinces shows a negative spatial spillover effect.;

Although the COVID-19 pandemic is no longer a life-threatening pandemic, it has left behind economic challenges worldwide. During the outbreak, many governments mandated the use of face masks in public areas, resulting in its demand and market price immediately skyrocketing. The government may intervene in markets through price, which may lead to market misallocation. This article aims to investigate how consumers form their beliefs about face mask price changes based on their estimations of the market demand and supply, both with and without government intervention, and assess their willingness-to-pay (WTP) for face masks during the pandemic. The data was collected through an online survey of Chinese consumers in 2020. The results provide valuable insights for policymakers to better understand consumers' price expectations and WTP for face masks, enabling more effective government interventions to maintain market stability while ensuring consumer access to face masks. [ABSTRACT FROM AUTHOR]

The vision transformer (ViT) has demonstrated performance comparable to that of convolutional neural networks (CNN) in the hyperspectral image classification domain. This is achieved by transforming images into sequence data and mining global spectral-spatial information to establish remote dependencies. Nevertheless, both the ViT and CNNs have their own limitations. For instance, a CNN is constrained by the extent of its receptive field, which prevents it from fully exploiting global spatial-spectral features. Conversely, the ViT is prone to excessive distraction during the feature extraction process. To be able to overcome the problem of insufficient feature information extraction caused using by a single paradigm, this paper proposes an MLP-mixer and a graph convolutional enhanced transformer (MGCET), whose network consists of a spatial-spectral extraction block (SSEB), an MLP-mixer, and a graph convolutional enhanced transformer (GCET). First, spatial-spectral features are extracted using SSEB, and then local spatial-spectral features are fused with global spatial-spectral features by the MLP-mixer. Finally, graph convolution is embedded in multi-head self-attention (MHSA) to mine spatial relationships and similarity between pixels, which further improves the modeling capability of the model. Correlation experiments were conducted on four different HSI datasets. The MGEET algorithm achieved overall accuracies (OAs) of 95.45%, 97.57%, 98.05%, and 98.52% on these datasets. [ABSTRACT FROM AUTHOR]

Simple Summary: Metastasis, the hallmark of cancer, is accountable for about 90% of cancer-related deaths. Long noncoding RNA (lncRNA), a subset of the noncoding RNAs, has exhibited involvement in various processes, including immune evasion, cell proliferation, migration, and invasion, across multiple human diseases, notably cancer. The objective of our study was to identify a metastasis-associated lncRNA in lung cancer and elucidate the underlying mechanisms. Our findings indicate that Linc01703, which is notably downregulated in metastatic lung cancer cells, effectively suppresses lung cancer metastasis in vivo. Interestingly, Linc01703 does not directly impact the proliferation and invasion capabilities of lung cancer cells but rather inhibits cancer metastasis by promoting the secretion of CD81+ exosomes through the Rab27a/SYTL1/CD81 transport complexes. Consequently, our observations provide new insights into the potential clinical application of CD81+ exosome-based cancer therapy. Metastasis, a major cause of cancer-related mortality worldwide, frequently occurs early in the diagnosis of lung adenocarcinoma (LUAD). However, the precise molecular mechanisms governing the aggressive metastatic behavior of LUAD remain incompletely understood. In this study, we present compelling evidence indicating that the long noncoding RNA linc01703 is significantly downregulated in metastatic lung cancer cells. Intriguingly, in vivo experiments revealed that Linc01703 exerted a profound inhibitory effect on lung cancer metastasis without discernible impact on the in vitro proliferation or invasion capacities of LUAD cells. Mechanistically, Linc01703 enhanced the interaction between Rab27a, SYTL1, and CD81, consequently promoting the secretion of CD81+ exosomes. These exosomes, in turn, suppressed the infiltration of immune cells within the tumor microenvironment, thereby impeding LUAD metastasis. Importantly, our analysis of lung cancer tissues revealed a correlation between reduced CD81 expression and an unfavorable patient prognosis. Collectively, our findings suggest that Linc01703 functions as a metastasis suppressor by facilitating the secretion of CD81+ exosomes through the formation of the Rab27a/SYTL1/CD81 complex. [ABSTRACT FROM AUTHOR]

Computational Fluid Dynamics (CFD) technology was used to analyse dispersion effects of 3 plates of different structures during adhesives production. Three models of the dispersion plates were designed: (1) Project 1: a plate with sharp and short teeth, (2) Project 2: a plate with sharp and long teeth, (3) Project 3: a plate with blunt and long teeth. Pictures of velocity flow contours and flow trajectories in the CFD analysis were obtained to show the flow conditions inside the Reactor. Particle studies were also run to predict adhesive particles dispersion effects of these 3 kinds of plates, so that the best structure of the plate would be chosen.

The implementation of the virtual water strategy (VWS) transporting invisible water resources through product scheduling faces resistance due to limited reporting and understanding and the lack of motivation analysis for stakeholders. This study builds a semi‐quantitative Hotelling game model under different scenarios to analyse the influence of preference and material benefits on potential acceptance of VWS with policymakers and stakeholders. Equilibrium analyses of the game show that human preference can be as important as real benefits. With preference differences, it is hard to make all stakeholders accept or reject a VWS approach in achieving optimal results for environment and social welfare. To implement a sustainable VWS mode, modifying preferences through propaganda and education can be effective. The natural play of the game with modified preferences will ultimately favour a holistic VWS approach to responsible management. This model supports the effectiveness of game theory in the implementation of a VWS. [ABSTRACT FROM AUTHOR]

Agriculture has become one of the main sources of carbon emissions in China, and studying the dynamic evolution of agricultural carbon emission intensity and carbon compensation potential can provide guidance for agricultural carbon peaking and carbon neutrality. Based on the provincial panel data from 2001-2018, we firstly analyzed the dynamic evolution of China's agricultural carbon emission intensity using Gini coefficient and kernel density, then tested the 'catch-up effect' of China's agricultural carbon compensation rate using spatial correlation test and absolute convergence, and finally the regional differences in China's agricultural carbon offset potential were measured by the parameter comparison method. The results showed that: ➀ The mean value of Gini coefficient of agricultural carbon emission intensity in China was 0.039, and the overall difference showed a 'U' shape, indicating that the overall relative difference of agricultural carbon emission intensity in China had an obvious trend of fluctuation and evolution. Agricultural carbon emission intensity in all provinces had a decreasing trend, but the decreasing trend was not consistent in different regions, and the decreasing speed of agricultural carbon emission intensity was faster in the eastern and central regions of China, followed by the national and western regions. ➁ The Moran's I of China's agricultural carbon offset rate from 2001 to 2018 was positive overall, Geary's C was less than 1 overall, and the LISA aggregation distribution showed a high-high and low-low aggregation trend. All these results indicated that there was a significant positive spatial correlation in China's agricultural carbon offset rate, and the high (low) neighboring provinces were relatively clustered, showing a relatively strong spatial clustering pattern. ➂ The absolute convergence results showed that there was a 'catching-up effect' in agricultural carbon offset rates at both the national level and the three regional levels. The parametric comparison method under the convergence theory showed that there were obvious regional differences in China's agricultural carbon offset potential, and the western region had the largest carbon offset potential with an average carbon offset potential of 42.72%, which was significantly higher than the average carbon offset potential in the eastern (27.05%) and the central region (29.40%). In intra-regional terms, the provinces with the highest carbon offset potential in the eastern, central and western regions were Fujian (59.23%), Hubei (47.63%) and Gansu (64.85%), respectively. Therefore, focusing on promoting agricultural quality and efficiency and strengthening inter -regional coordination and cooperation can achieve China's agricultural carbon peaking and carbon neutrality goals as soon as possible. [ABSTRACT FROM AUTHOR]