Search Results (155)

The European Union’s (EU) Common Agricultural Policy aims to promote sustainable farming practices that ensure the responsible use of natural resources, safeguard biodiversity, and uphold higher animal welfare standards. One pathway to achieving these objectives is through the encouragement of extensive farming. However, the dairy sector in EU countries as well as in Lithuania has shown a clear trend toward intensification. The aim of this study was to assess the technical efficiency (TE) of dairy farms employing extensive and intensive technologies. TE was evaluated using Data Envelopment Analysis (DEA) combined with meta-frontier analysis, which accounts for technological heterogeneity. Prior to the efficiency estimation, farms were grouped into two distinct categories—intensive and extensive—using the k-means clustering algorithm. The empirical results show that extensive dairy farms in Lithuania are smaller in land area and livestock units, rely more on internal resources, and exhibit lower productivity compared to intensive farms. Intensive farms achieved higher technical efficiency, narrower technological gaps, and more optimal scale efficiency, indicating superior resource management. The weaker performance of extensive farms is attributed to both less advanced technologies and production inefficiencies. Full article

Nanozymes constitute a rapidly advancing frontier in scientific research, attracting widespread international interest, particularly for their role in facilitating cascade reactions. Despite their initial discovery a few years ago, significant hurdles persist in optimizing their catalytic performance and substrate specificity—challenges that are especially critical in the context of biomedical diagnostics. Within this domain, nitrogen-containing graphene oxide-based nanozymes exhibiting peroxidase-mimicking activity have emerged as particularly promising candidates, owing to the exceptional electrical conductivity, mechanical flexibility, and structural resilience of reduced graphene oxide-based materials. Intensive efforts have been devoted to engineering graphene oxide structures to enhance their peroxidase-like functionality. Nonetheless, the practical implementation of such nanozymes remains under active investigation and demands further refinement. This review synthesizes the current developments in nitrogen heteroatom-containing graphene oxide nanozymes and their derivative nanozymes, emphasizing recent breakthroughs and biomedical applications. It concludes by exploring prospective directions and the broader potential of these materials in the biomedical landscape. Full article

T-2 toxin, a type A trichothecene mycotoxin produced by Fusarium species, is widely present in cereals and their processed products, posing a significant contaminant in food safety. To address the food safety challenges caused by this toxin, we established a dual signal enhancement by magnetic separation and split aptamer for ultrasensitive T-2 toxin detection. In this method, the introduction of magnetic graphene oxide (MGO) enhanced signal and increased sensitivity by reducing background interference. The shortened split aptamer reduces non-specific binding to MGO via decreased steric hindrance, thereby facilitating rapid target-induced dissociation and signal generation. A FAM fluorophore-labeled split aptamer probe FAM-SpA1-1 was quenched by MGO. While the fluorescence intensity remained nearly unchanged when the unlabeled split aptamer probe SpA1-2 was introduced alone, a significant fluorescence recovery was observed upon simultaneous addition of SpA1-2 and T-2 toxin. This recovery resulted from the cooperative binding of SpA1-1 and SpA1-2 to T-2 toxin, which distanced the FAM-SpA1-1 probe from MGO. Therefore, the proposed biosensor demonstrated excellent stability, reproducibility, and specificity, with a linear response range of 10–500 pM and a limit of detection (LOD) of 0.83 pM. Satisfactory recovery rates were achieved in spiked wheat (86.0–114.2%) and beer (112.0–129.6%) samples, highlighting the biosensor’s potential for practical applications in real-sample detection. This study establishes the T-2 toxin split aptamer and demonstrates a novel dual-signal enhancement paradigm that pushes the sensitivity frontier of aptamer-based mycotoxin sensors. Full article

Gonghe Basin is an important frontier of resource and energy development and environmental protection on the Qinghai–Tibetan Plateau and upper sections of the Yellow River. As a characteristic ecotone, this area exhibits complex and diverse ecosystem types while demonstrating marked ecological vulnerability. The response of ecosystem services (ESs) to human activities (HAs) is directly related to the sustainable construction of an ecological civilization highland and the decision-making and implementation of high-quality development. However, this response relationship is unclear in the Gonghe Basin. Based on remote sensing data, land use, meteorological, soil, and digital elevation model data, the current research determined the human activity intensity (HAI) in the Gonghe Basin by reclassifying HAs and modifying the intensity coefficient. Employing the InVEST model and bivariate spatial autocorrelation methods, the spatiotemporal evolution characteristics of HAI and ESs and responses of ESs to HAs in Gonghe Basin from 2000 to 2020 were quantitatively analyzed. The results demonstrate that: From 2000 to 2020, the HAI in the Gonghe Basin mainly reflected low-intensity HA, although the spatial range of HAI continued to expand. Single plantation and town construction activities exhibited high-intensity areas that spread along the northwest-southeast axis; composite activities such as tourism services and energy development showed medium-intensity areas of local growth, while the environmental supervision activity maintained a low-intensity wide-area distribution pattern. Over the past two decades, the four key ESs of water yield, soil conservation, carbon sequestration, and habitat quality exhibited distinct yet interconnected characteristics. From 2000 to 2020, HAs were significantly negatively correlated with ESs in Gonghe Basin. The spatial aggregation of HAs and ESs was mainly low-high and high-low, while the aggregation of HAs and individual services differed. These findings offer valuable insights for balancing and coordinating socio-economic development with resource exploitation in Gonghe Basin. Full article

It is of great significance to clarify the impact of the rapid development of digital trade on China’s grain imports in order to enhance its efficiency and guarantee food security. From an import perspective, this article adopts a stochastic frontier gravity model and a trade inefficiency model to analyze the influence of global digital trade development on the efficiency of China’s grain imports and further estimates the potential for trade expansion. The main findings include the following: (a) Divergence in digital trade capabilities persists across nations. As countries advance their digital trade ecosystems, China’s grain import efficiency demonstrates corresponding enhancements. (b) Compared with digital infrastructure construction and digital trade competition intensity, China’s food import trade efficiency increases as the level of digital technology innovation improves. (c) China achieves the highest trade efficiency in grain import among the ASEAN (Association of Southeast Asian Nations) and North American countries, while the greatest untapped potential lies in imports from South America. Accordingly, for different countries, it is necessary to adopt different strategies to enhance cooperation with the world’s major grain-trading countries in the areas of digital trade infrastructure construction and digital technology innovation, and to use digital trade to optimize China’s grain import trade chain and improve its efficiency. Full article

Artificial intelligence (AI) and machine learning (ML) are rapidly transforming clinical decision support systems (CDSSs) in intensive care units (ICUs), where vast amounts of real-time data present both an opportunity and a challenge for timely clinical decision-making. Here, we trace the evolution of machine intelligence in critical care. This technology has been applied across key ICU domains such as early warning systems, sepsis management, mechanical ventilation, and diagnostic support. We highlight a transition from rule-based systems to more sophisticated machine learning approaches, including emerging frontier models. While these tools demonstrate strong potential to improve predictive performance and workflow efficiency, their implementation remains constrained by concerns around transparency, workflow integration, bias, and regulatory challenges. Ensuring the safe, effective, and ethical use of AI in intensive care will depend on validated, human-centered systems supported by transdisciplinary collaboration, technological literacy, prospective evaluation, and continuous monitoring. Full article

With the rapid development of intelligent transportation systems, obtaining vehicle status information across large-scale road networks is essential for the coordinated management and control of traffic conditions. Distributed Acoustic Sensing (DAS) demonstrates considerable potential in vehicle status perception due to its characteristics such as high spatial resolution and robustness in complex sensing environments. This study first reviews the limitations of conventional vehicle detection technologies and introduces the operating principles and technical features of DAS. Secondly, it investigates the correlations between DAS sensing characteristics, deployment process, and driving behavior characteristics. The results indicate that both the intensity of driving behavior and the degree of deployment–process coupling are positively associated with DAS signal sensing characteristics. This study further examines the principles, advantages, limitations, and application scenarios of various DAS signal processing algorithms. Traditional methods are becoming less effective in handling massive data generated by numerous distributed nodes. Although deep learning achieves high classification accuracy and low latency, its generalization capability remains limited. Finally, this study discusses DAS-based traffic status perception frameworks and outlines key research frontiers in vehicle status monitoring using DAS technology. Full article

This study proposes a three-level meta-frontier framework enhanced with machine learning-driven projection methods to address the dual heterogeneity in carbon emission efficiency analysis arising from regional disparities and industrial diversification. Methodologically, we introduce two novel projection combinations—“exogenous-exogenous-accumulation (E-E-A) and exogenous-exogenous-consistent (E-E-C)”—to resolve the inconsistency of technology gap ratios (TGRs > 1) in traditional nonradial directional distance function (DDF) models. Reinforcement learning (RL) optimizes dynamic direction vectors, whereas graph neural networks (GNNs) encode spatial interdependencies to constrain the TGR within [0, 1]. Empirical analysis of 60 countries reveals that (1) E-E-C eliminates the TGR overestimation by 12–18% in energy-intensive sectors (e.g., reducing Asia’s secondary industry $TG{R}_1$ from 1.160 to 1.000); (2) industrial heterogeneity dominates inefficiency in Asia (IHI = 0.207), whereas management gaps drive global secondary sector inefficiency (MI = 0.678); and (3) policy simulations advocate for decentralized renewables in Africa, fiscal incentives for Asian coal retrofits, and expanded EU carbon border taxes. Computational enhancements via Apache Spark achieve a 58% runtime reduction. The framework advances environmental efficiency analysis by integrating machine learning with meta-frontier theory, offering both methodological rigor (via regularization and GNN constraints) and actionable decarbonization pathways. Limitations include static heterogeneity assumptions and data granularity gaps, prompting the future integration of IoT-enabled dynamic models. Full article

While the impact of trade openness on economic growth has been widely studied, its effect on township economies remains underexplored. In view of this empirical gap, this study examines the impact of trade openness proxied by export intensity on the technical efficiency of five major township economies in South Africa—Soweto, Khayelitsha, Alexandra, Tembisa, and Soshanguve—while considering the moderating role of information and communication technology (ICT). This aim speaks to the ongoing quest to unravel factors limiting the transformation of South African townships since the advent of democracy in 1994. The analysis uses an instrumental variable stochastic frontier model and annual panel data covering the 1995–2023 period. On average, the five townships were found to have operated 19% below their full potential during the sampling period, with Soweto being the least efficient. Holding constant factors peculiar to township economies, such as crime rates and informality, the main results show that ICT plays a positive moderating role in reducing trade-related technical inefficiencies of these townships. This finding underscores the importance of targeted policy interventions, such as investments in digital infrastructure and digital literacy programs, to ensure that township economies benefit from global markets and achieve their full potential. Full article

Forest and grass systems are globally significant carbon-sequestering ecosystems, crucial for mitigating climate change and optimizing ecological management. To clarify the research history, major contributing groups, and research hotspots related to carbon sequestration in global forest and grass systems, this study utilizes the core ensemble of the Web of Science database as its data source. Employing bibliometric methodology and software, such as VOSviewer 1.6.20 and CiteSpace 5.7.R1, we analyzed the development of 594 relevant publications from 2010 to 2024, focusing on their developmental lineage, research groups, current research status, and visualizing and analyzing research hotspots and frontiers. The results indicate that the volume of the literature on carbon sequestration in forest and grass systems generally follows the pattern of a logistic growth curve, demonstrating an upward trend from 2010 to 2024. The primary contributors consist of 400 researchers, including Nath, Arun Jyoti, and Ajit, as well as 378 research organizations across 42 countries, including China, the USA, and India. China’s contribution to this field is rapidly increasing, accounting for over 20% of the total articles, with ‘Chinese Acad Sci’ and ‘Univ Chinese Acad Sci’ being the most prominent contributors, together representing 10.45% of the total publications in this field. The 179 journals, including Agroforestry Systems and Forests, serve as a significant platform for academic exchange in the development of this field. The predominant research directions are found in the areas of ‘Environmental Sciences & Ecology’ and ‘Agriculture’, which collectively account for over 50% of the publications. Additionally, research focused on ‘Sequestration’ is increasingly examining the relationship between carbon sequestration in forest and grassland systems and factors such as climate change, ecosystem productivity, and biodiversity. The keyword clusters ‘#0 ferralsol’ and ‘#4 forest ecosystem’ have consistently represented important research directions throughout this period. A total of 21 keywords were identified, with ‘land use change’ exhibiting the highest intensity at 4.4524. Future research should not only prioritize the integration of the impacts of global climate change but also enhance collaboration among authors and institutions. Furthermore, it is essential to promote multidisciplinary and cross-regional collaborative innovations by leveraging emerging technologies such as AI and genetic engineering. Full article

Enantioselective transition metal catalysis is undoubtedly a cornerstone at the frontier of chemistry, attracting intense interest from both academia and the pharmaceutical industry. Central to this field is the strategic utilization of noncovalent interactions (NCIs), including hydrogen bonding, ion pairing, and π-system engagements, which not only drive asymmetric synthesis but also enable precise stereochemical control in transition metal-catalyzed transformations. Recent breakthroughs have unveiled a new generation of rationally designed ligands that exploit ligand–substrate noncovalent interactions, emerging as indispensable tools for stereocontrolled synthesis and setting new paradigms in ligand engineering. These advancements establish a transformative framework for ligand engineering, bridging fundamental mechanistic insights with practical synthetic utility. In this review, the judicious design concepts and syntheses of novel ligands from the past five years were highlighted and their synthetic applications in asymmetric catalysis were detailed. Full article

This study analyzes the crude oil supply in 2030 and 2050, comparing it with demand scenarios from the UN Intergovernmental Panel on Climate Change and the International Energy Agency. It focuses on the oil under production or development as of today (or the supply already under contract), and the oil frontiers. For that, it firstly evaluates a database of over 107,000 assets to identify and classify recoverable oil volumes through 2050. By comparing the supply and demand, this study identifies scenarios requiring production declines or, in opposition, the development of new projects and exploratory frontiers. The focus is on 2030 and 2050, which are key milestones in the global climate agenda. As an original contribution, the analysis also identifies how oil supply regions position themselves regarding oil quality, production costs, and the GHG emission intensity of the oil offered. As the second contribution, this study develops the probability assessment of recoverable resources to evaluate a typical oil frontier, analyzing how global climate scenarios could affect the probability of approving a deepwater offshore project. The findings show that cumulative oil consumption by 2050 may range from 600 billion to 1 trillion barrels, with marginal supply costs between US$28/bbl and US$44/bbl. The findings indicate that the frontier project lacks economic attractiveness in scenarios limiting the increase in the global surface temperature (GST) below 1.5 °C with no or limited overshoots. However, assuming a smooth price decline trajectory from today to 2050, the project exhibits high profitability and returns across all the scenarios. This suggests that the industry might remain inclined to approve new projects, even amid potential energy transition scenarios, driven by favorable short- and medium-term returns despite long-term uncertainties. Full article

Muon collisions are considered a promising means for exploring the energy frontier, leading to a detailed study of the possible feasibility challenges. Beam intensities of the order of ${10}^{12}$ muons per bunch are needed to achieve the necessary luminosity, generating a high flux of secondary and tertiary particles from muons decay that reach both the machine elements and the detector region. To limit the impact of this background on the physics performance, tungsten shieldings have been studied. A machine learning-based approach to the geometry optimization of these shieldings will be discussed. Full article

This review explores two main topics: the state-of-the-art and emerging capabilities of high-peak-power, ultrafast (picosecond and femtosecond) long-wave infrared (LWIR) laser technology based on CO₂ gas laser amplifiers, and the current and advanced scientific applications of this laser class. The discussion is grounded in expertise gained at the Accelerator Test Facility (ATF) of Brookhaven National Laboratory (BNL), a leading center for ultrafast, high-power CO₂ laser development and a National User Facility with a strong track record in high-intensity physics experiments. We begin by reviewing the status of 9–10 μm CO₂ laser technology and its applications, before exploring potential breakthroughs, including the realization of 100 terawatt femtosecond pulses. These advancements will drive ongoing research in electron and ion acceleration in plasma, along with applications in secondary radiation sources and atmospheric energy transport. Throughout the review, we highlight how wavelength scaling of physical effects enhances the capabilities of ultra-intense lasers in the LWIR spectrum, expanding the frontiers of both fundamental and applied science. Full article