Evaluating carbon emissions reduction compliance based on 'dual control' policies of energy consumption and carbon emissions in China.

Dual control policies aim to effectively reduce energy consumption and carbon emission by setting targets for total volume and intensity control. With global climate change becoming increasingly serious. China, as a large energy-consuming and carbon-emitting country, faces a huge challenge to reduce carbon emissions. The transition from the "dual control of energy consumption" to the "dual control of carbon emissions" in China is necessary to optimize energy structures, promote energy conservation, reduce carbon emissions, and reach carbon neutrality. This study utilizes multi-regional input-output models to evaluate the results toward both goals from production-based, consumption-based, and income-based perspectives. The findings indicate that "dual control of carbon emissions" is a more precise approach than "dual control of energy consumption". Some provinces have met the latter goal while still falling short of the former. Provinces having met their production-based energy intensity and carbon emission intensity targets, but having failed their consumption-based and income-based targets, are identified to develop a comprehensive and accurate assessment of these targets. A net outflow of embodied energy and carbon emissions is observed among provinces in less-developed central and southern regions to more-developed eastern and northern regions. Suggestions and policy implications based on these findings include establishing a comprehensive evaluation of energy and carbon intensity, considering both consumption-based and income-based perspectives, as well as facilitating enhanced cooperation among developed and developing provinces.

Glyphosate contamination in European rivers not from herbicide application?

The most widely used herbicide glyphosate contaminates surface waters around the globe. Both agriculture and urban applications are discussed as sources for glyphosate. To better delineate these sources, we investigated long-term time series of concentrations of glyphosate and its main transformation product aminomethylphosphonic acid (AMPA) in a large meta-analysis of about 100 sites in the USA and Europe. The U.S. data reveal pulses of glyphosate and AMPA when the discharge of the river is high, likely indicating mobilization by rain after herbicide application. In contrast, European concentration patterns of glyphosate and AMPA show a typical cyclic-seasonal component in their concentration patterns, correlating with patterns of wastewater markers such as pharmaceuticals, which is consistent with the frequent detection of these compounds in wastewater treatment plants. Our large meta-analysis clearly shows that for more than a decade, municipal wastewater was a very important source of glyphosate. In addition, European river water data show rather high and constant base mass fluxes of glyphosate all over the year, not expected from herbicide application. From our meta-analysis, we define criteria for a source of glyphosate, which was hidden so far. AMPA is known to be a transformation product not only of glyphosate but also of aminopolyphosphonates used as antiscalants in many applications. As they are used in laundry detergents in Europe but not in the USA, we hypothesize that glyphosate may also be a transformation product of aminopolyphosphonates.

Effect of genotype and nutritional and environmental challenges on growth curve dynamics of broiler chickens.

The present study aimed to compare the dynamics of growth of various chicken genotypes exposed to heat stress, low-input diets, and free-range farming by using Gompertz model to gain insights into their capabilities to face environmental and nutritional challenges. Three in vivo trials (T1: heat stress, T2: low-input diets, and T3: free-range system) were conducted, involving a total of 671 animals. Five chicken genotypes were employed in each trial: 2 Italian local breeds, Bionda Piemontese (BP) and Robusta Maculata (RM), along with their crossbreeds with Sasso hens (BP×SA and RM×SA), and a commercial hybrid (Ross 308). One-day-old male chicks were individually identified, and the 5 genotypes were randomly allocated to different challenging conditions: T1 involved 2 environmental temperatures (thermoneutral vs. high temperature); T2 involved 2 diets (standard vs. low-input); T3 involved 2 rearing systems (conventional vs. free-range). The chickens were weighed once a week from their arrival until slaughtering, and the data were used to build growth curves using the Gompertz model. Chickens from different genotypes were slaughtered at varying ages based on their maturity. In all trials, the challenging conditions significantly reduced adult body weight (A; -31.0%) and maximum growth rate (MGR; -25.6%) of Ross chickens. In contrast, in T1 and T2, no significant changes were observed in the main growth curve parameters of local breeds and crossbreeds, while under free-range conditions, there was even an increase in the A and MGR of these genotypes. The crossbreeding was effective in increasing A and MGR of BP (+30.5% in BP×SA), as well as in improving the precocity and MGR of RM (+19.5% in RM×SA). Our findings highlight the effectiveness of the Gompertz model as a tool for evaluating birds' adaptability and confirm the greater ability of local breeds and crossbreeds to adapt to different challenges. In conclusion, our methodological approach could be used to choose the genotype most suited to the environmental context and confirm the potential advantages of crossbreeding for enhancing resilience and sustainability.

A high-efficiency poly-input boost DC-DC converter for energy storage and electric vehicle applications.

This research paper introduces an avant-garde poly-input DC-DC converter (PIDC) meticulously engineered for cutting-edge energy storage and electric vehicle (EV) applications. The pioneering converter synergizes two primary power sources-solar energy and fuel cells-with an auxiliary backup source, an energy storage device battery (ESDB). The PIDC showcases a remarkable enhancement in conversion efficiency, achieving up to 96% compared to the conventional 85-90% efficiency of traditional converters. This substantial improvement is attained through an advanced control strategy, rigorously validated via MATLAB/Simulink simulations and real-time experimentation on a 100 W test bench model. Simulation results reveal that the PIDC sustains stable operation and superior efficiency across diverse load conditions, with a peak efficiency of 96% when the ESDB is disengaged and an efficiency spectrum of 91-95% during battery charging and discharging phases. Additionally, the integration of solar power curtails dependence on fuel cells by up to 40%, thereby augmenting overall system efficiency and sustainability. The PIDC's adaptability and enhanced performance render it highly suitable for a wide array of applications, including poly-input DC-DC conversion, energy storage management, and EV power systems. This innovative paradigm in power conversion and management is poised to significantly elevate the efficiency and reliability of energy storage and utilization in contemporary electric vehicles and renewable energy infrastructures.

Arterial input function estimation compensating for inflow and partial voluming in dynamic contrast-enhanced MRI.

Both inflow and the partial volume effect (PVE) are sources of error when measuring the arterial input function (AIF) in dynamic contrast-enhanced (DCE) MRI. This is relevant, as errors in the AIF can propagate into pharmacokinetic parameter estimations from the DCE data. A method was introduced for flow correction by estimating and compensating the number of the perceived pulse of spins during inflow. We hypothesized that the PVE has an impact on concentration-time curves similar to inflow. Therefore, we aimed to study the efficiency of this method to compensate for both effects simultaneously. We first simulated an AIF with different levels of inflow and PVE contamination. The peak, full width at half-maximum (FWHM), and area under curve (AUC) of the reconstructed AIFs were compared with the true (simulated) AIF. In clinical data, the PVE was included in AIFs artificially by averaging the signal in voxels surrounding a manually selected point in an artery. Subsequently, the artificial partial volume AIFs were corrected and compared with the AIF from the selected point. Additionally, corrected AIFs from the internal carotid artery (ICA), the middle cerebral artery (MCA), and the venous output function (VOF) estimated from the superior sagittal sinus (SSS) were compared. As such, we aimed to investigate the effectiveness of the correction method with different levels of inflow and PVE in clinical data. The simulation data demonstrated that the corrected AIFs had only marginal bias in peak value, FWHM, and AUC. Also, the algorithm yielded highly correlated reconstructed curves over increasingly larger neighbourhoods surrounding selected arterial points in clinical data. Furthermore, AIFs measured from the ICA and MCA produced similar peak height and FWHM, whereas a significantly larger peak and lower FWHM was found compared with the VOF. Our findings indicate that the proposed method has high potential to compensate for PVE and inflow simultaneously. The corrected AIFs could thereby provide a stable input source for DCE analysis.

Optimizing control efficiency in discrete-time multi-agent systems via event-triggered containment techniques combining disturbance handling and input delay management.

The goal of this paper is to mitigate disturbances and input delays while optimizing controller actuation updates for discrete-time multi-agent systems through the use of an event-triggered confinement control system, especially in resource-constrained scenarios. This approach when combined with event-triggered control techniques, then every follower in the system adjusts its condition at specified times based on an event-triggered condition that is suggested. The containment control system issue in the presence of disturbances and input delays was tackled by using both decentralized and centralized event-triggered control systems. Using matrix theory and the Lyapunov technique, convergence analysis is conducted to show that the proposed strategy stays free of zeno phenomena. Numerical boosts are used to further illustrate the impact of theoretical results.

Curiosity model policy optimization for robotic manipulator tracking control with input saturation in uncertain environment.

In uncertain environments with robot input saturation, both model-based reinforcement learning (MBRL) and traditional controllers struggle to perform control tasks optimally. In this study, an algorithmic framework of Curiosity Model Policy Optimization (CMPO) is proposed by combining curiosity and model-based approach, where tracking errors are reduced via training agents on control gains for traditional model-free controllers. To begin with, a metric for judging positive and negative curiosity is proposed. Constrained optimization is employed to update the curiosity ratio, which improves the efficiency of agent training. Next, the novelty distance buffer ratio is defined to reduce bias between the environment and the model. Finally, CMPO is simulated with traditional controllers and baseline MBRL algorithms in the robotic environment designed with non-linear rewards. The experimental results illustrate that the algorithm achieves superior tracking performance and generalization capabilities.

Incidental Learning of Collocations Under Different Input Modes and the Mediating Role of Perceptual Learning Style.

This study investigated how input modes (reading vs. listening) and learners' perceptual learning style (visual vs. auditory) affected the incidental learning of collocations. A total of 182 college students were first assigned to either a visual or auditory group based on their performance on a perceptual learning style questionnaire. Each style group was subsequently subdivided into three groups who were exposed to a series of texts containing unfamiliar collocation items under one of the input conditions: written input, aural input, or no input. Results of the study indicated that both written and aural input led to gains in collocational knowledge, and aural input was more effective than written input. Furthermore, the study provided empirical evidence that there was a moderating role of perceptual learning style on incidental collocation learning. The auditory learners under aural input showed the highest rate of collocation learning among all treatment subgroups.

Optimal IOFL-based economic model predictive control technique for boiler-turbine system.

The optimal control design of the boiler-turbine system is vital to ensure feasibility and high responsiveness over desired load variations. Using the traditional linear control techniques realization of this task is difficult, as the boiler-turbine mechanism has strong nonlinearities. Besides, environmental and economic concerns have replaced existing tracking control ones as the primary concerns of advanced power plants. Thus, this study proposes an optimal economic model predictive controller (EMPC) scheme for this unit on the basis of the input/output feedback linearization (IOFL) method. By employing the IOFL method, this unit is decoupled into a new linearized model that is utilized for developing the suggested optimal IOFL EMPC technique. The proposed control scheme is formulated in an economic quadratic programming form that considers the input-rate and input limits of the unit for optimal economic performance. In addition, an adaptive iterative algorithm is utilized for constraints mapping with guaranteeing a feasible solution in a finite number of steps without violation of original constraints over the entire predictive horizon. The outcomes of the simulation show that the suggested optimal IOFL EMPC scheme offers an improved dynamic and economic output performance over fuzzy hierarchical MPC, fuzzy EMPC, and nonlinear EMPC techniques during various load variations.

Design of Miniaturized and Wideband Four-Port MIMO Antenna Pair for WiFi.

A miniaturized and wideband four-port multiple-input multiple-output (MIMO) antenna pair for Wi-Fi mobile terminals application is proposed. The proposed antenna pair consists of four multi-branch antenna elements arranged orthogonally, with an overall size of 40 × 40 × 3.5 mm3 and each antenna element size of 15.2 × 3.5 mm × 0.8 mm3. The performance of the proposed antenna shows the advantages of a wide frequency band, low mutual coupling, high efficiency, and a compact structure. The wideband characteristics of the antenna elements are achieved through multi-mode resonance. The suppression of coupling is accomplished by strategically positioning the four compact antenna elements to ensure their maximum radiation directions are orthogonal, thus eliminating the need for an additional decoupling structure. In this paper, the proposed antenna is optimized in terms of the parameters then simulated and measured. The simulated results illustrate that an impedance bandwidth of the antenna is about 15% (5.06~5.88 GHz) with S11 < -10 dB, excellent port isolation exceeds 20 dB between all ports, a high radiation efficiency ranges from 51.2% to 89.9%, the maximum gain is 4.5 dBi, and the ECCs are less than 0.04. The measured results show that the -10 dB impedance bandwidth of the antenna is about 13% (5.13~5.80 GHz), the isolation between the antenna elements is better than 21 dB, the radiation efficiency ranges from 51.8% to 92.3%, the maximum gain is 5.3 dBi, and the ECCs are less than 0.05. The proposed four-port MIMO antenna works on the 5G LTE band 46 and Wi-Fi 6E operating bands. As a mobile terminal antenna, the proposed design scheme demonstrates excellent performance and applicability, fulfilling the requirements for 5G mobile terminal applications.

Split_ Composite: A Radar Target Recognition Method on FFT Convolution Acceleration.

Synthetic Aperture Radar (SAR) is renowned for its all-weather and all-time imaging capabilities, making it invaluable for ship target recognition. Despite the advancements in deep learning models, the efficiency of Convolutional Neural Networks (CNNs) in the frequency domain is often constrained by memory limitations and the stringent real-time requirements of embedded systems. To surmount these obstacles, we introduce the Split_ Composite method, an innovative convolution acceleration technique grounded in Fast Fourier Transform (FFT). This method employs input block decomposition and a composite zero-padding approach to streamline memory bandwidth and computational complexity via optimized frequency-domain convolution and image reconstruction. By capitalizing on FFT's inherent periodicity to augment frequency resolution, Split_ Composite facilitates weight sharing, curtailing both memory access and computational demands. Our experiments, conducted using the OpenSARShip-4 dataset, confirm that the Split_ Composite method upholds high recognition precision while markedly enhancing inference velocity, especially in the realm of large-scale data processing, thereby exhibiting exceptional scalability and efficiency. When juxtaposed with state-of-the-art convolution optimization technologies such as Winograd and TensorRT, Split_ Composite has demonstrated a significant lead in inference speed without compromising the precision of recognition.

Input-to-state stability of delayed memristor-based inertial neural networks via non-reduced order method.

This paper is concerned with the input-to-state stability (ISS) for a kind of delayed memristor-based inertial neural networks (DMINNs). Based on the nonsmooth analysis and stability theory, novel delay-dependent and delay-independent criteria on the ISS of DMINNs are obtained by constructing different Lyapunov functions. Moreover, compared with the reduced order approach used in the previous works, this paper consider the ISS of DMINNs via non-reduced order approach. Directly analysis the model of DMINNs can better maintain its physical backgrounds, which reduces the complexity of calculations and is more rigorous in practical application. Additionally, the novel proposed results on the ISS of DMINNs here incorporate and complement the existing studies on memristive neural network dynamical systems. Lastly, a numerical example is provided to show that the obtained criteria are reliable.

Specific and comprehensive genetic targeting reveals brain-wide distribution and synaptic input patterns of GABAergic axo-axonic interneurons.

Axo-axonic cells (AACs), also called chandelier cells (ChCs) in the cerebral cortex, are the most distinctive type of GABAergic interneurons described in the neocortex, hippocampus, and basolateral amygdala (BLA). AACs selectively innervate glutamatergic projection neurons (PNs) at their axon initial segment (AIS), thus may exert decisive control over PN spiking and regulate PN functional ensembles. However, the brain-wide distribution, synaptic connectivity, and circuit function of AACs remain poorly understood, largely due to the lack of specific and reliable experimental tools. Here, we have established an intersectional genetic strategy that achieves specific and comprehensive targeting of AACs throughout the mouse brain based on their lineage (Nkx2.1) and molecular (Unc5b, Pthlh) markers. We discovered that AACs are deployed across essentially all the pallium-derived brain structures, including not only the dorsal pallium-derived neocortex and medial pallium-derived hippocampal formation, but also the lateral pallium-derived claustrum-insular complex, and the ventral pallium-derived extended amygdaloid complex and olfactory centers. AACs are also abundant in anterior olfactory nucleus, taenia tecta, and lateral septum. AACs show characteristic variations in density across neocortical areas and layers and across subregions of the hippocampal formation. Neocortical AACs comprise multiple laminar subtypes with distinct dendritic and axonal arborization patterns. Retrograde monosynaptic tracing from AACs across neocortical, hippocampal, and BLA regions reveal shared as well as distinct patterns of synaptic input. Specific and comprehensive targeting of AACs facilitates the study of their developmental genetic program and circuit function across brain structures, providing a ground truth platform for understanding the conservation and variation of a bona fide cell type across brain regions and species.

Whether we are memorising facts or reacting to a loud noise, nerve cells in different brain areas must be able to communicate with one another through precise, meaningful signals. Specialized nerve cells known as interneurons act as "traffic lights" to precisely regulate when and where this information flows in neural circuits. Axo-axonic cells are a rare type of inhibitory interneuron that are thought to be particularly important for controlling the passage of information between different groups of excitatory neurons. This is because they only connect to one key part of their target cell ­ the axon-initial segment ­ where the electrical signals needed for brain communication (known as action potentials) are initiated. Since axo-axonic cells are inhibitory interneurons, this connection effectively allows them to 'veto' the generation of these signals at their source. Although axo-axonic cells have been identified in three brain regions using traditional anatomical methods, there were no 'tags' readily available that can reliably identify them. Therefore, much about these cells remained unknown, including how widespread they are in the mammalian brain. To solve this problem, Raudales et al. investigated which genes are switched on in axo-axonic cells but not in other cells, identifying a unique molecular signature that could be used to mark, record, and manipulate these cells. Microscopy imaging of brain tissue from mice in which axo-axonic cells had been identified revealed that they are present in many more brain areas than previously thought, including nearly all regions of the broadly defined cerebral cortex and even the hypothalamus, which controls many innate behaviors. Axo-axonic cells were also 'wired up' differently, depending on where they were located; for example, those in brain areas associated with memory and emotions had wider-ranging input connections than other areas. The finding of Raudales et al. provide, for the first time, a method to directly track and manipulate axo-axonic cells in the brain. Since dysfunction in axo-axonic cells is also associated with neurological disorders like epilepsy and schizophrenia, gaining an insight into their distribution and connectivity could help to develop better treatments for these conditions.

Anthropocene 129I Record in the Yellow Sea Sediments and Its Indication for River-Delivered Radioactive Pollution to Marginal Seas.

As the number of coastal nuclear facilities rapidly increases and the wastewater from the Fukushima Nuclear Plant has been discharged into the Pacific Ocean, the nuclear environmental safety of China's marginal seas is gaining increased attention along with the heightened potential risk of nuclear accidents. However, insufficient work limits our understanding of the impact of human nuclear activities on the Yellow Sea (YS) and the assessment of their environmental process. This study first reports the 129I and 127I records of posthuman nuclear activities in the two YS sediments. Source identification of anthropogenic 129I reveals that, in addition to the gaseous 129I release and re-emission of oceanic 129I discharged from the European Nuclear Fuel Reprocessing Plants (NFRPs), the Chinese nuclear weapons testing fallout along with the global fallout is an additional 129I input for the continental shelf of the YS. The 129I/127I atomic ratios in the North YS (NYS) sediment are significantly higher than those in the other adjacent coastal areas, attributed to the significant riverine input of particulate 129I by the Yellow River. Furthermore, we found a remarkable 129I latitudinal disparity in the sediments than those in the seawaters in the various China seas, revealing that sediments in China's marginal seas already received a huge anthropogenic 129I from terrigenous sources via rivers and thus became a significant sink of anthropogenic 129I. This study broadens an insight into the potential impacts of terrigenous anthropogenic pollution on the Chinese coastal marine radioactive ecosystem.

The carbon footprint of health care delivery in Western Australia's public health system.

Background: Health systems have a dual imperative to take action on climate change. First, they must develop climate resilient health services in response to the direct and indirect impacts of climate change on health. Second, they must reduce their own carbon footprint since health systems are a significant contributor to global greenhouse gas emissions. Methods: An environmentally-extended multi-region input-output analysis was carried out, incorporating National Accounts data for Australia and annual expenditure data from WA Health for financial year 2019-20. Expenditure data were categorised to one of 344 economic sectors and by location of the provider of goods or services purchased. Findings: WA Health contributes 8% of WA's total carbon footprint, driven by expenditure on chemicals (23.8% of total), transport (20.2% of total), and electricity supply (19.7% of total). These 3 sectors represent 63.7% of WA Health's carbon footprint, but only 10.8% of its total expenditure. Interpretation: Reducing emissions related to health service provision in WA will require a holistic approach that leverages carbon footprinting insights and integrates them into organisational decision-making across all health programs. The high carbon-intensity of the transport and chemicals sectors supports previous research calling for a reduction in unnecessary pathology testing and the transition to delivery of non-urgent health care via sustainable models of telehealth. The impact of WA's size and location presents challenges, with a predominantly non-renewable energy supply and reliance on transport and supply chains from other states adding significantly to emissions. Funding: The study received funding from the Australian Research Council, The University of Sydney, and the WA Department of Health. The full list of funding information can be found in Acknowledgements.

A signal smoothness-based approach to prescribed performance control of strict-feedback nonlinear systems with quantization.

This paper investigates the prescribed performance control problems of strict-feedback nonlinear systems with state quantization, input quantization, unknown disturbances and unknown nonlinear functions. Since the quantized states are discontinuous, the differentiability of the stabilizing functions in the backstepping technique cannot be guaranteed. To this end, a smooth approximation of the quantized states is first obtained by introducing a class of functions. Based on this smooth approximation, a quantized control scheme is presented such that all the closed-loop signals are bounded with the prescribed performance bounds. It is shown that the unknown nonlinearities and the unknown disturbances are not estimated and the derivatives of the stabilizing functions are eliminated. Lastly, two examples are provided to illustrate the effectiveness of the presented method.

Aggravating Pollution of Emerging Aryl Organophosphate Esters in Urban Estuarine Sediments of South China.

Emerging aryl organophosphate esters (aryl-OPEs) have been employed as substitutes for organohalogen flame retardants in recent years; however, their environmental occurrence and associated impacts in urban estuarine sediments have not been adequately investigated, impeding regulatory decision-making. Herein, field-based investigations and modeling based on surface sediment and sediment core analysis were employed to uncover the historical pollution and current environmental impacts of aryl-OPEs in the Pearl River Estuary, South China. Our results revealed a substantial increase in aryl-OPE emission, particularly emerging aryl-OPEs, through sediment transport to the estuary since the 2000s. The emerging aryl-OPEs comprised 83% of the total annual input in the past decade, with an average annual input of 155,000 g. Additionally, the emerging-to-traditional aryl-OPE concentration ratios increased with decreasing distance from the shore, peaking in the highly urbanized riverine outlets. These findings indicate that inventories of emerging aryl-OPEs are likely increasing in estuarine sediments and their emissions are surpassing those of traditional aryl-OPEs. Our risk-based priority screening approach indicates that some emerging aryl-OPEs, particularly bisphenol A bis(diphenyl phosphate), can pose a higher environmental risk than traditional aryl-OPEs in estuarine sediments. Overall, our study highlights the importance of recognizing the environmental impacts of emerging aryl-OPEs.

Duration of cochlear implant use in children with prelingual single-sided deafness is a predictor of word perception in the CI ear.

As part of a longitudinal study regarding the benefit of early cochlear implantation for children with single-sided deafness, the current work explored the children's daily device use, potential barriers to full-time device use, and the children's ability to understand speech with the cochlear implant (CI). Data were collected from 20 children with prelingual SSD who received a CI before the age of 2.5 years, from the initial activation of the sound processor until the children were 4.8 to 11.0 years old. Daily device use was extracted from the CI's data logging, while word perception in quiet was assessed using direct audio input to the children's sound processor. The children's caregivers completed a questionnaire about habits, motivations, and barriers to device use. The children with SSD and a CI used their device on average 8.3 h per day, corresponding to 63 % of their time spent awake. All children except one could understand speech through the CI, with an average score of 59 % on a closed-set test and 73 % on an open-set test. More device use was associated with higher speech perception scores. Parents were happy with their decision to pursue a CI for their child. Certain habits, like taking off the sound processor during illness, were associated with lower device use. Providing timely counselling to the children's parents, focused on SSD-specific challenges, may be helpful to improve daily device use in these children.

An empirical study on new-energy vehicle users' preferences of in-vehicle interaction input methods.

New energy sources are transforming the automotive market. This shift has also expanded the possibilities for in-vehicle interaction. Through a literature review, this study categorizes the in-vehicle interaction activities into three types: driving tasks, comfort tasks, and entertainment tasks. This study conducted empirical survey of 377 users to understand their preferences of in-vehicle interaction input methods inside new energy vehicles. The results show that gender, educational level, income, driver's license type and driving experience have significant influence on the perception and preference of the in-vehicle interaction input methods. However, age and experience with new energy vehicle didn't show significant results. The findings of this study can assist manufacturers in developing targeted solutions and meeting the personalized needs of users in future vehicle market segments.

The food security risks in the Yangtze River Delta of China associated with water scarcity, grain production, and grain trade.

Grain production consumes a large amount of water and is affected by the degree of water scarcity and participation in the grain trade in various regions. The grain trade has changed the food security risks in regions where grain exports and imports. Therefore, it is crucial to consider regional water scarcity to understand food security risks from the grain trade network. Here, we construct a new framework for measuring regional food security risks associated with water scarcity, grain production, and grain trade based on a cross-city grain trade network combined with virtual water flows to evaluate the regional food security risks in the Yangtze River Delta region (YRD) of China in 2017. The results show that under the current domestic grain trade pattern in China, the YRD and its four provincial-level administrative regions are in a net grain import state. The grain trade within the YRD is concentrated in exports from the two major grain-producing areas of Anhui and Jiangsu to Zhejiang and Shanghai, especially from northern Jiangsu to southeastern Zhejiang. The net import results of virtual blue water in most cities indicate that the YRD has shifted its water resource pressure to other grain exporting regions in China, with Shanghai and Zhejiang being the greatest beneficiaries. Extreme risk only exists in Shanghai, and severe and moderate risks are concentrated in Jiangsu. The current grain trade has reduced the overall food security risk in the YRD by 1.3 % but increased the risks in Shanghai and Zhejiang by 2.1 % and 0.8 % respectively. This study highlights the potential risks that excessive production of food in water-scarce areas in the grain trade system may bring to a stable food supply, providing useful information for a comprehensive understanding of the food and water security situation and for future trade-offs.