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With the application of digital technology and its promotion of business model innovation, digital transformation has increasingly become an important strategic issue for enterprises. In this context, based on imprinting theory, we select all A-share listed enterprises in China from 2008 to 2022 as samples and study the relationship between the academic background of senior executives and the digital transformation of enterprises. The study results show that senior executives with academic backgrounds can significantly promote the digital transformation of enterprises. A test of the action mechanism shows that the academic background of senior executives plays a role in the promotion of the digital transformation of enterprises by improving enterprise innovation, and the degree of industry competition moderates the relationship between the academic background of senior executives and the digital transformation of enterprises. This paper applies imprint theory to explore the relationship between the academic background of executives and corporate digital transformation, expanding the research on how imprints affect corporate decision-making and the scope of imprint theory research, while also providing evidence to support government departments in formulating policies to encourage talented individuals with academic backgrounds to participate in corporate management.

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Mechanical testing machines are used to evaluate kinematics, kinetics, wear, and efficacy of spinal implants. The simulation of "physiological" spinal loading conditions necessitates the simultaneous use of multiple actuators. The challenge in achieving a desired loading profile lies in achieving close synchronization of these actuators. Errors in load application can be attributed to both the control system and the intrinsic sample response. Moreover, the presence of friction in the setup can have an impact on the measured outcome. The optimization of setup parameters can substantially improve the ability to simulate spinal loading conditions and obtain reliable data on implant performance. In this study, a reproducible kinematic test protocol was developed to evaluate the sensitivity of the kinetic response (i.e., measured loads, moments, and stiffnesses) of a cervical disc prosthesis to several testing parameters. In this context, five ceramic ball and socket sample implants were mounted in a 6 DOF material testing machine and tested with a constant axial compressive force of 100 N in two motion modes: 1) flexion-extension (±7.5°) and 2) lateral bending (±6°). Parameters including rotation rate, slider friction, friction between the samples' articulating surfaces, and moment arm were considered to determine their effects on measured kinetic parameters. The sensitivity analysis indicated that all setup parameters except friction between the samples' articulating surfaces had a substantial effect on the results. The findings were then compared to predictions from a free body diagram to determine the optimal setup parameters. Consequently, the setup with the lowest rotation rate and employing passive sliders yielded results that were consistent with the free body diagram. This study demonstrated the significance of a comprehensive setup evaluation for reliable and reproducible testing of spinal implants, also for comparison between labs.

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The purpose of this scoping review was to report the effects of vibration therapy, electrical stimulation, and transcranial magnetic stimulation on patients with muscle dystrophies. The outcome measures were muscle strength, body composition, balance, and functional mobility of these patients. We used the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and the Arksey and O'Malley framework. The literature review was conducted on PubMed. We included studies that were written in English, were peer-reviewed, without regard to the publication date, and implemented a form of "vibration therapy" or "electrical stimulation" or "magnetic stimulation" as an intervention program of any duration. Overall, 14 studies were retrieved. Most of the studies applied whole-body vibration (WBV) therapy or electrical stimulation and only one was found that implemented transcranial magnetic stimulation. The interventions were reported but there was a variety in duration or the frequency of the program, as well as in the disease progression of the patients. It seems that WBV, electrical stimulation, and magnetic stimulation have positive outcomes, but these vary depending on the muscle deficits and limitations of the patients with muscle dystrophy. It is recommended that future studies should be conducted in order to determine the ideal prescription of each intervention, so as to be as beneficial as possible.

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BACKGROUND AND OBJECTIVES: This survey study aimed to (1) identify patient/family research priorities in pediatric-onset multiple sclerosis (POMS), and (2) delineate optimized methods for research study/clinical trials design, engagement, and implementation. METHODS: Participants were as follows: (1) parents of a child (<18 years) with POMS enrolled in a national registry, (2) adolescents (13-17 years) with POMS in the registry, and (3) adults (18-40 years) with POMS receiving care at a registry affiliated clinic. Of 293 eligible participants, 192 completed surveys. RESULTS: Experiences with health care and medications were generally positive but there remain areas of priority improvement. Incentives to participate in clinical trials included medications previously tested and in pill form, bloodwork/study visits required ⩾ every 3 months, cognitive testing ⩽1 hour, compensation for travel and time, ability to continue current multiple sclerosis (MS) medication, option to take study medication if on placebo, and individualized study feedback. Priorities for clinical research were (1) psychosocial impact, (2) cognitive/academic impact, (3) environmental risk, and (4) nutrition. CONCLUSIONS: Results highlighted the importance of a holistic approach to study design and a focus on the impact of disease on daily life to best engage patients and families in POMS clinical trials and research.

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Global Value Chains (GVCs) significantly influence international trade and environmental outcomes. Despite the economic benefits of GVCs, their impact on the environment remains under-examined. This study  analyzes the effects of GVC participation (considering forward and backward positions) on total carbon emissions embodied in exports (TEEE) and imports (TEEI). Utilizing panel data from 65 economies spanning 1995 to 2018, we apply input-output matrices and the system generalized method of moments (GMM-SYS) approach. Our results indicate that GVC participation generally is associated with reductions in TEEI and increases in TEEE, with backward participation exerting a more substantial impact. Furthermore, we observed asymmetrical impacts of GVC participation between developed and developing countries. Developed nations tend to reap greater benefits from GVC in terms of diminished CO2 emissions associated with imports, as well as notable CO2 reductions in both exports and imports, particularly when forward participation is the focal point. Conversely, developing countries grapple with heightened environmental burdens stemming from their engagement in backward linkages.

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The regulation of genes can be mathematically described by input-output functions that are typically assumed to be time invariant. This fundamental assumption underpins the design of synthetic gene circuits and the quantitative understanding of natural gene regulatory networks. Here, we found that this assumption is challenged in mammalian cells. We observed that a synthetic reporter gene can exhibit unexpected transcriptional memory, leading to a shift in the dose-response curve upon a second induction. Mechanistically, we investigated the cis-dependency of transcriptional memory, revealing the necessity of promoter DNA methylation in establishing memory. Furthermore, we showed that the synthetic transcription factor's effective DNA binding affinity underlies trans-dependency, which is associated with its capacity to undergo biomolecular condensation. These principles enabled modulating memory by perturbing either cis- or trans-regulation of genes. Together, our findings suggest the potential pervasiveness of transcriptional memory and implicate the need to model mammalian gene regulation with time-varying input-output functions. A record of this paper's transparent peer review process is included in the supplemental information.

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Promoting good health and ensuring responsible production and consumption are essential components of the Sustainable Development Goals (SDGs) established by of the United Nations, as well as the goals of beautiful China. While the health impacts of air pollution have garnered significant attention, there remains a paucity of studies comparing the disparities in responsibility arising from production versus consumption. This paper integrates the Weather Research and Forecasting - Comprehensive Air Quality Model with Extensions (WRF-CAMx) model, the multiregional input‒output (MRIO) model, and the global exposure mortality model (GEMM) to assess the extent of PM2.5-related premature deaths caused by production and consumption activities in 30 Chinese provinces. The findings reveal a spatial mismatch in health burdens between production and consumption. Considering pollutant emissions and their transfer only through the supply chain leads to the finding that the net outflow of emissions from producers is mainly located in most of the northern provinces of China. However, when atmospheric transport and health impacts are included, the producing provinces are mainly located in central China, while the consuming provinces are located in the southeastern coastal and remote western and northern regions. Additionally, the long-range impact of consumption provinces with respect to the health burden is more than twice as large as that of production provinces, and its potential impact on the health burden cannot be ignored. From a sectoral perspective, production emissions from the non-electricity industry and services sectors contribute to 60% of the health burden, while their consumption emissions contribute to over 80% of the health burden. Furthermore, consumption activities in the non-electricity industry and services sectors significantly influence production emissions in the transport, agriculture, and electricity sectors. The geographical separation of consumption and production regions facilitated by trade is a critical yet often overlooked aspect in current regional air quality planning in China. A more comprehensive analysis of life-cycle emissions driven by final consumption could yield greater reductions compared to direct production reductions.

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Realizing a synergistic reduction of air pollutant and CO2 emissions (APCE) is an important approach to promote a green socio-economic transformation in China, and it can provide a solid foundation for the achievement of clean energy production and climate action under a sustainable development goal framework. The objective of this study is to explore the quantitative relationship and evolution of synergies between APCE in industrial sectors driven by different socio-economic effects from 2007 to 2020 in China. The results indicated that the main sectors of pollutant emissions had consistency, however, large differences in the reduction efficiency of emissions exist among pollutants. The efficiency in reducing CO2 emissions was about 48% lower when compared with reductions of SO2 (95%), NOx (86%), and smoke and dust (83%) emissions from 2007 to 2020. The effects of improved technology were the main contributor to a reduction in pollutant emissions, but the synergies between APCE driving by it were not achieved. While the synergies between APCE driven by structure and final demand effects were significant. The synergies between NOx and CO2 emissions were stronger driven by final demand structure and type effects, with correlation coefficients of 1.06 and 1.13, respectively. Besides, the degree of synergistic reduction between APCE in most industrial sectors was around zero. Therefore, the efficiency of synergistic pollution reduction should be improved with the development of a synergistic governance system for industrial sectors. The structural decomposition analysis based on input-output model combined with the cross-elasticity analysis method to quantitively synergies between APCE from the consumption (demand) perspective, considering the connections between industrial sectors with socio-economic developing, which would contribute to the industrial synergistic reduction and green transformation as the consumption driven gradually increasing.

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Climate-driven changes in freshwater inputs have been shown to affect the structure and function of coastal ecosystems. We evaluated changes in the influence of river runoff on coastal systems of Northwestern Patagonia (NWP) over recent decades (1993-2021) by combined analysis of long-term streamflow time series, hydrological simulation, satellite-derived and reanalysis data on sea surface conditions (temperature, turbidity, and salinity). Significant decreases in minimum streamflow across a zone spanning six major river basins were evident at weekly, monthly, and seasonal scales. These changes have been most pronounced in mixed-regime northern basins (e.g., Puelo River) but appear to be progressing southward to rivers characterised by a nival regime. In the adjacent two-layer inner sea, reduced freshwater input corresponds with a shallower halocline and increased surface temperatures across northern Patagonia. Our results underscore the rapidly evolving influence of rivers on adjacent estuarine and coastal waters in NWP. We highlight the need for cross-ecosystem observation, forecasting, mitigation and adaptation strategies in a changing climate, together with corresponding adaptive basin management of systems that supply runoff to the coastal marine waters.

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Healthcare is a major generator of greenhouse gases, so consideration of this contribution to climate change needs to be quantified in ways that can inform models of care. Given the availability of activity-based financial data, environmentally-extended input-output (EEIO) analysis can be employed to calculate systemic carbon footprints for healthcare activities, allowing comparison of different patient care pathways. We thus quantified and compared the carbon footprint of two common care pathways for patients with stable coronary artery disease, with similar clinical outcomes: coronary stenting and coronary artery bypass surgery (CABG). Healthcare cost data for these two pathways were disaggregated and the carbon footprint associated with this expenditure was calculated by connecting the flow of money within the economy to the greenhouse gases emitted to support the full range of associated activities. The systemic carbon footprint associated with an average stable patient CABG pathway, at a large tertiary referral hospital in Sydney, Australia in 2021-22, was 11.5 tonnes CO2-e, 4.9 times greater than the 2.4 tonnes CO2-e footprint of an average comparable stenting pathway. These data suggest that a stenting pathway for stable coronary disease should be preferred on environmental grounds and introduces EEIO analysis as a practical tool to assist in health-care related carbon footprinting.

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In this paper, we propose a new data-aided (DA) joint angle and delay (JADE) maximum likelihood (ML) estimator. The latter consists of a substantially modified and, hence, significantly improved gray wolf optimization (GWO) technique by fully integrating and embedding within it the powerful importance sampling (IS) concept. This new approach, referred to hereafter as GWOEIS (for "GWO embedding IS"), guarantees global optimality, and offers higher resolution capabilities over orthogonal frequency division multiplex (OFDM) (i.e., multi-carrier and multi-path) single-input multiple-output (SIMO) channels. The traditional GWO randomly initializes the wolfs' positions (angles and delays) and, hence, requires larger packs and longer hunting (iterations) to catch the prey, i.e., find the correct angles of arrival (AoAs) and time delays (TDs), thereby affecting its search efficiency, whereas GWOEIS ensures faster convergence by providing reliable initial estimates based on a simplified importance function. More importantly, and beyond simple initialization of GWO with IS (coined as IS-GWO hereafter), we modify and dynamically update the conventional simple expression for the convergence factor of the GWO algorithm that entirely drives its hunting and tracking mechanisms by accounting for new cumulative distribution functions (CDFs) derived from the IS technique. Simulations unequivocally confirm these significant benefits in terms of increased accuracy and speed Moreover, GWOEIS reaches the Cramér-Rao lower bound (CRLB), even at low SNR levels.

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Income group heterogeneity and transboundary food-related water footprints are essential for water resource management. Previous studies have not fully characterized the transboundary food-related water footprints by regional income groups. Taking Guangdong as an example, this study calculates the local and transboundary food-related water footprints by income groups and explores relevant socioeconomic factors during 2007-2017. Results show that the proportion of transboundary food-related water footprints by income groups has increased during 2007-2017. By 2017, nearly half of food-related water footprints of income groups happened in external regions. In particular, the high-income groups of Guangdong transferred large amounts of food-related water footprints to specific northern regions (e.g., Heilongjiang and Jilin). However, socioeconomic changes of these northern regions contributed to the increase of food-related water footprints by income groups. Fortunately, the transitions of food consumption structures of income groups helped to reduce the external food-related water footprints. We also observed that the effects of dietary behavior changes were group heterogeneous. The findings of this study can provide scientific foundations for group-targeted dietary behavior optimization to reduce water footprints, as well as interregional collaboration for sustainable food and water resource management.

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Hormones mediate inter-organ signaling which is crucial in orchestrating diverse behaviors and physiological processes including sleep and activity, feeding, growth, metabolism and reproduction. The pars intercerebralis and pars lateralis in insects represent major hubs which contain neurosecretory cells (NSC) that produce various hormones. To obtain insight into how hormonal signaling is regulated, we have characterized the synaptic connectome of NSC in the adult Drosophila brain. Identification of neurons providing inputs to multiple NSC subtypes implicates diuretic hormone 44-expressing NSC as a major coordinator of physiology and behavior. Surprisingly, despite most NSC having dendrites in the subesophageal zone (primary taste processing center), gustatory inputs to NSC are largely indirect. We also deciphered pathways via which diverse olfactory inputs are relayed to NSC. Further, our analyses revealed substantial inputs from descending neurons to NSC, suggesting that descending neurons regulate both endocrine and motor output to synchronize physiological changes with appropriate behaviors. In contrast to NSC inputs, synaptic output from NSC is sparse and mostly mediated by corazonin NSC. Therefore, we additionally determine putative paracrine interconnectivity between NSC subtypes and hormonal pathways from NSC to peripheral tissues by analyzing single-cell transcriptomic datasets. Our comprehensive characterization of the Drosophila neurosecretory network connectome provides a platform to understand complex hormonal networks and how they orchestrate animal behaviors and physiology.

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In this study, the fluxes of microplastics (mp) were quantified during a 12-month period for three rural headwater lake catchments in Muskoka-Haliburton, south-central Ontario, Canada. A novel catchment particle balance approach was used, incorporating inputs from atmospheric deposition and stream inflows against lake outflow and sedimentation. This approach provides the first reported observation-based estimates of microplastic residence time in freshwater lakes. Atmospheric deposition had the highest daily microplastic flux (3.95-8.09 mp/m2/day), compared to the inflow streams (2.21-2.34 mp/m2/day), suggesting that it is the dominant source of microplastics to rural regions. Approximately 44-71% of the deposited microplastics were retained in the terrestrial catchments and 30-49% of the microplastics in the stream inflows were retained in the study lakes. Given that output fluxes ranged from 0.72-3.76 mp/m2/day in the sediment and 1.18-1.66 mp/m2/day in the lake outflows, the microplastic residence time was estimated to be between 3 and 12 years, suggesting that lakes are an important reservoir for microplastics. Fibers were the dominant shape in atmospheric deposition, streamwater, and lake water; however, in lake sediment, there was a higher proportion of fragments. Across all media, poly(ethylene terephthalate) was the dominant polymer identified (23%).

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Significance: The arterial input function (AIF) plays a crucial role in correcting the time-dependent concentration of the contrast agent within the arterial system, accounting for variations in agent injection parameters (speed, timing, etc.) across patients. Understanding the significance of the AIF can enhance the accuracy of tissue vascular perfusion assessment through indocyanine green-based dynamic contrast-enhanced fluorescence imaging (DCE-FI). Aim: We evaluate the impact of the AIF on perfusion assessment through DCE-FI. Approach: A total of 144 AIFs were acquired from 110 patients using a pulse dye densitometer. Simulation and patient intraoperative imaging were conducted to validate the significance of AIF for perfusion assessment based on kinetic parameters extracted from fluorescence images before and after AIF correction. The kinetic model accuracy was evaluated by assessing the variability of kinetic parameters using individual AIF versus population-based AIF. Results: Individual AIF can reduce the variability in kinetic parameters, and population-based AIF can potentially replace individual AIF for estimating wash-out rate ( k ep ), maximum intensity ( I max ), ingress slope with lower differences compared with those in estimating blood flow, volume transfer constant ( K trans ), and time to peak. Conclusions: Individual AIF can provide the most accurate perfusion assessment compared with assessment without AIF or based on population-based AIF correction.

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The booming digital economy is promoting industrial upgrading and transformation, providing an opportunity for the industrial low-carbon development. Based on input-output analysis and panel data regression, the impact of the input of intermediate digital products on Chinese industrial carbon intensity from 1997 to 2017 has been evaluated. The results reveal that the input of China's intermediate digital products can significantly reduce industrial carbon intensity, and significantly reduce the carbon intensity of non-energy-intensive industries. It has also been discovered that the input of intermediate digital product manufacturing can significantly reduce the carbon intensity of different types of industries, while the input of the intermediate digital products services industry has no significant effect on the reduction of industrial carbon intensity. Channel analysis shows that the input of the intermediate digital products can reduce industrial carbon intensity by improving productive efficiency and promoting innovative technology. In the current climate, it is especially necessary to increase the input of the intermediate digital product manufacturing industry in industrial development.

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This paper investigates safety-critical event-triggered control (ETC) for nonlinear systems. It proposes a new ETC strategy that ensures event-triggered safety by using a tunable input-to-state safe barrier function. Then, a method is devised to effectively save communication resources and reduce computational load through the construction of a suitable dynamic event-triggered mechanism. Furthermore, the exclusion of Zeno behavior is guaranteed for the closed-loop system. Finally, this paper culminates by presenting two illustrative examples that showcase the effectiveness of proposed control strategies.

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The widespread adoption of video-based applications across various fields highlights their importance in modern software systems. However, in comparison to images or text, labelling video test cases for the purpose of assessing system accuracy can lead to increased expenses due to their temporal structure and larger volume. Test prioritization has emerged as a promising approach to mitigate the labeling cost, which prioritizes potentially misclassified test inputs so that such inputs can be identified earlier with limited time and manual labeling efforts. However, applying existing prioritization techniques to video test cases faces certain limitations: they do not account for the unique temporal information present in video data. Unlike static image datasets that only contain spatial information, video inputs consist of multiple frames that capture the dynamic changes of objects over time. In this paper, we propose VRank, the first test prioritization approach designed specifically for video test inputs. The fundamental idea behind VRank is that video-type tests with a higher probability of being misclassified by the evaluated DNN classifier are considered more likely to reveal faults and will be prioritized higher. To this end, we train a ranking model with the aim of predicting the probability of a given test input being misclassified by a DNN classifier. This prediction relies on four types of generated features: temporal features (TF), video embedding features (EF), prediction features (PF), and uncertainty features (UF). We rank all test inputs in the target test set based on their misclassification probabilities. Videos with a higher likelihood of being misclassified will be prioritized higher. We conducted an empirical evaluation to assess the performance of VRank, involving 120 subjects with both natural and noisy datasets. The experimental results reveal VRank outperforms all compared test prioritization methods, with an average improvement of 5.76% ∼ 46.51% on natural datasets and 4.26% ∼ 53.56% on noisy datasets.

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In karst areas, the dissolved inorganic carbon (DIC) concentrations in aquatic systems are typically higher than that in non-karst areas due to intensive carbonate rock weathering. Understanding the sources and input fluxes of DIC in karst reservoirs is crucial for regional carbon cycle studies. This study utilized dual carbon isotopes (δ13CDIC and Δ14CDIC) to estimate the contribution rates and input fluxes of DIC from various sources in Aha Reservoir (AHR), located in southwestern China. Our results indicated that the DIC concentrations (22.33-32.79 mg L-1) and δ13CDIC values (-10.02‰ to -8.55‰) were nearly homogeneous both vertically and laterally in the reservoir (p > 0.05). The Δ14CDIC values (-246.31‰ to -137.86‰) were homogeneous along the vertical profile (p > 0.05), but showed significant horizontal variation (p < 0.05), with values decreasing from -149.57 ± 10.27‰ to -232.85 ± 2.37‰ at the mouths of the inflowing rivers. We found that the inflowing rivers were the primary DIC sources to AHR, contributing 70% of the total input, while groundwater and atmospheric CO2 contributions were relatively minor, at 18% and 12%, respectively. The Jinzhong River (JZR), influenced by industrial and domestic wastewater discharge, contributed the largest DIC input flux at 2.01 t/(km2·mon). In contrast, the Youyu River (YYR), influenced by acidic mine drainage, and the Baiyan River (BYR), influenced by agricultural activities, contributed relatively smaller DIC input fluxes of 1.29 t/(km2·mon) and 1.03 t/(km2·mon), respectively. This study highlights the significant impact of anthropogenic activities on DIC input in AHR, with industrial and domestic wastewater discharges having a greater influence than agricultural activities and acidic mine wastewater inputs. These findings underscore the critical need to manage and mitigate the impacts of human activities on karst reservoir ecosystems.

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Global Net Anthropogenic Nitrogen Input (NANI) at high resolution is crucial for assessing the impact of human activities on aquatic environments. Insufficient global high-resolution data sources and methods have hindered the effective examination of the global characteristics and driving forces of NANI. This study presents a general framework for calculating global NANI, providing estimates at a 5-arc-minute resolution and over 1.42 million lake basins in 2015. The results highlight the region near the Tropic of Cancer as a concentration area for high NANI and an inflection point for latitude-based accumulation variation. It also emphasizes the uneven distribution of NANI among continents, with Asia and Africa having the highest proportions, yet their high and low values are notably lower than those of Europe and South America. A similar pattern is observed in global lakes, where Asia has the smallest quantity and volume, but the highest NANI intensity. In contrast, North America and Europe have larger quantities and volumes but the lowest NANI intensity. The global distribution characteristics reveal a clustering pattern in high and low values, with 1.25 % of the area having a sum of NANI exceeding 20 %. The uncertainty analysis regarding model parameters indicates that continents with the highest NANI do not always exhibit the highest uncertainty. These results bridge the gap between global nitrogen sustainable management and anthropogenic nitrogen input. They support research on spatiotemporal changes and controlling factors of global river nutrient loads, as well as the impact of climatic factors on basin nitrogen loss and its variability.