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Study the circumferential contour of the deformation zone in the strip warm rolling, according the flow stress characteristic of warm deformation, the contact arc of deformation area is divided into three different curvature arc, the roller elastic flattening model of deformation zone is established using the method of quadratic curve fitting. The roller circumferential contour was calculated that iterative solver the roller elastic flattening deformation area and the distribution of rolling force along the rolling direction. Deformation process of warm-rolling was simulated using ANSYS/LS-DYNA finite element, the trend with simulation and calculated in the forward and backward slip zone is accordance, the model is more consistent with deformation process of the strip warm rolling. [ABSTRACT FROM AUTHOR]

Improving the spatial pattern of agricultural systems has become a promising approach for enhancing agricultural productivity and sustainability. However, previous studies have often focused on the influence of natural factors on crop distribution, ignoring factors such as human activities, socio-economic level and ecological environment. This study aims to investigate the influence of natural factors and social environmental drivers on the optimal pattern of multiple crops and evaluate the potential of optimal patterns to enhance agricultural productivity and sustainability. Here, we present a multi-criteria approach integrating natural and social environment system factors and set up three assessment scenarios: crop growth suitability (CGS), crop planting suitability (CPS), and crop planting competitiveness (CPC). Applying this approach to the Shiyang River basin in China as a case study, we assessed the suitability and competitiveness of single crops. To maximize agriculture competitiveness, we optimized the structure of the multi-crop system, and a method was proposed to identify highly suitable intercropping areas using crop competitiveness conflicts. Regional crop water consumption, water productivity, and economic benefits were calculated to analyze the potential for agricultural intensification under different optimization patterns. We found that the weights of four categories factors of location, socio-economic conditions, productivity, and environment protection accounted for 26.9%, 7.5%, 13.7%, and 4.5%, respectively, and the weight of social environmental influence indicators had accounted for about 43.5%, which cannot be ignored. The distribution area above moderate suitability (L2) in the CPS and CPC scenarios was about 7.92% - 30.03% and 6.14% - 26.4% higher than the CGS scenario, respectively. Social environmental factors are important to consider in assessing the suitability of crops. From the spatial structure, three optimization patterns all suggested increasing the planting proportion of wheat and potato in the future. The optimization patterns in CPC scenario could reduce total crop water demand by 91.86 to 175.77 million cubic meters compared with 2020 while showing great potential to improve crop water productivity and net output per cubic meter of water. Furthermore, we offered recommendations for the layouts of common intercropping systems in Northwest China based on the proposed method for identifying high suitability zones. This study emphasizes the importance of considering multiple environments to accurately assess crop suitability and achieve sustainable agricultural. The results could provide useful insights for managing and optimizing diverse planting systems, addressing growing concerns surrounding food and water security in resource-constrained regions. [Display omitted] • Human activities and social environment have not received sufficient attention for their impacts on agricultural patterns. • Proposed a novel approach to crop suitability assessment and structural optimization integrating natural and social systems. • Optimized patterns under the CPC scenario reduced water consumption and improved water productivity and net water benefits. • Findings emphasize the importance of considering human activities and social environmental drivers in agricultural planning. • Crop management based on dual environmental assessments provides a practical solution to promote sustainable agriculture. [ABSTRACT FROM AUTHOR]

Background The autonomic nervous system response to apnea and its mechanistic connection to atrial fibrillation (AF) are unclear. We hypothesize that sensory neurons within the ganglionated plexi (GP) play a role. We aimed to delineate the autonomic response to apnea and to test the effects of ablation of cardiac sensory neurons with resiniferatoxin (RTX), a neurotoxic TRPV1 (transient receptor potential vanilloid 1) agonist. Methods Sixteen dogs were anesthetized and ventilated. Apnea was induced by stopping ventilation until oxygen saturations decreased to 80%. Nerve recordings from bilateral vagal nerves, left stellate ganglion, and anterior right GP were obtained before and during apnea, before and after RTX injection in the anterior right GP (protocol 1, n=7). Atrial effective refractory period and AF inducibility on single extrastimulation were assessed before and during apnea, and before and after intrapericardial RTX administration (protocol 2, n=9). GPs underwent immunohistochemical staining for TRPV1. Results Apnea increased anterior right GP activity, followed by clustered crescendo vagal bursts synchronized with heart rate and blood pressure oscillations. On further oxygen desaturation, a tonic increase in stellate ganglion activity and blood pressure ensued. Apnea-induced effective refractory period shortening from 110.20±31.3 ms to 90.6±29.1 ms ( P<0.001), and AF induction in 9/9 dogs versus 0/9 at baseline. After RTX administration, increases in GP and stellate ganglion activity and blood pressure during apnea were abolished, effective refractory period increased to 126.7±26.9 ms ( P=0.0001), and AF was not induced. Vagal bursts remained unchanged. GP cells showed cytoplasmic microvacuolization and apoptosis. Conclusions Apnea increases GP activity, followed by vagal bursts and tonic stellate ganglion firing. RTX decreases sympathetic and GP nerve activity, abolishes apnea's electrophysiological response, and AF inducibility. Sensory neurons play a role in apnea-induced AF. [ABSTRACT FROM AUTHOR]

Due to population growth, environmental pollution and climate change, the lack of water resources has become a critical factor which threatens sustainable agricultural development. Reasonable irrigation scheduling strategies can reduce the waste of water and enhance agricultural water-use efficiency. In the present study, the decomposition-coordination theory was adopted to analyze the hierarchical canal system. A novel nonlinear multi-level multi-objective optimization model for complex canal systems was established, taking account of the multiple demands from decision makers and realistic factors of canal operation. An interactive method of the technique for order preference using similarity algorithm and genetic algorithm was proposed to solve the developed model. The developed model was successfully applied for the operational strategy making of a canal system located in the arid area of northwest China. The results indicated that the optimization model could help shorten the operational duration by two days, achieve about 26% reduction of irrigation water consumption, and improve the efficiency of water delivery from 0.566 to 0.687. That will be very favorable for the promotion of the agricultural water productivity, the relief of water shortage crisis and the sustainable development of agriculture. The outcomes can provide a wide range of support for decision making and make irrigation decision-making more scientific and systematic. [ABSTRACT FROM AUTHOR]

Remote sensing technology is widely used to obtain evapotranspiration (ETa), but whether it can distinguish the differences in farmland energy balance components and ETa under different irrigation methods has not been studied. We used Landsat 8 data as the primary dataset to drive the METRIC model and inverted the surface parameters and ETa of the Shiyang River Basin from 2014 to 2018. After improving the METRIC model using Ta obtained by the regression method instead of interpolation to calculate the net radiation flux (Rn), R2 was improved from 0.45 to 0.53, and the RMSE was reduced from 61 W/m2 to 51 W/m2. The ETa estimation results on satellite overpass days performed well, with R2 equal to 0.93 and RMSE equal to 0.48 mm when compared with the Eddy covariance method (EC) observations. Subsequently, the different growth stages and daily average ETa estimates of maize were compared with three observations (water balance, WB; Bowen ratio and energy balance method, BREB; and EC). The daily estimates of ETa correlate well with the observations of BREB (R2BI = 0.82, R2DI = 0.92; RMSEBI = 0.46 mm/day, RMSEDI = 0.32 mm/day) and EC (R2BI = 0.85, R2DI = 0.92; RMSEBI = 0.45 mm/day, RMSEDI = 0.34 mm/day), and the estimation for drip irrigation was found to be better than for border irrigation. The total accuracy of the ETa estimation on the five-year overpass day of maize farmland reached R2 = 0.93 and RMSE = 0.48 mm. With sufficient remote sensing data, the 4-year average ETa of maize was 31 mm lower for DI than for BI, and the mean value of ETa obtained from the three observation methods was 40 mm. The METRIC model can be used to distinguish ETa differences between the two irrigation methods in maize farmlands. [ABSTRACT FROM AUTHOR]

Non-small cell lung cancer (NSCLC) is a malignant tumor that threatens human life and health. The development of a new NSCLC risk assessment model based on electronic medical records has great potential for reducing the risk of cancer recurrence. In this process, machine learning is a powerful method for automatically extracting risk factors and indicating impact weights for NSCLC deaths. However, when the number of samples reaches a certain value, it is difficult for machine learning to improve the prediction accuracy, and it is also challenging to use the characteristic data of subsequent patients effectively. Therefore, this study aimed to build a postoperative survival risk assessment model for patients with NSCLC that updates the model parameters and improves model accuracy based on new patient data. The model perspective was a combination of particle filtering and parameter estimation. To demonstrate the feasibility and further evaluate the performance of our approach, we performed an empirical analysis experiment. The study showed that our method achieved an overall accuracy of 92% and a recall of 71% for deceased patients. Compared with traditional machine learning models, the accuracy of the model estimated by particle filter parameters has been improved by 2%, and the recall rate for dead patients has been improved by 11%. Additionally, this study outcome shows that this method can better utilize subsequent patients' characteristic data, be more relevant to different patients, and help achieve precision medicine. [ABSTRACT FROM AUTHOR]

Crop models are widely used to assist in agricultural management decision-making and water productivity optimization. However, traditional crop models often depend on artificial and specific field management inputs, posing challenges in maintaining crops within a desired stress range. Consequently, the derived optimization schemes from these models become highly uncertain. Moreover, the complexity of the mechanisms involved and the multitude of parameters make it challenging to apply traditional crop models uniformly across various crops and regions. In this study, we have developed a novel crop model called WHCrop (Water-Heat Driven Crop model) that effectively captures, reflects, and controls the impact of various environmental factors (meteorology, topography, soil, and management) on crop growth process. The WHCrop model combines the simulation principles of biomass and yield from the CERES module in the DSSAT model, along with the soil water balance from the AquaCrop model, to estimate the dynamics of crop growth and production processes. Results indicated that WHCrop-based simulations, including canopy cover (CC), daily evapotranspiration (ET), and yield, matched well with ground-based measurements, and were better than the traditional crop model (DSSAT and AquaCrop) at both field and regional scales, especially under deficient irrigation conditions. Besides capturing the key variables associated with crop growth, WHCrop model could reproduce the adaptive response of these various to regional-scale temperature changes. Notably, the WHCrop model could effectively minimize uncertainties resulting from individual environmental change, thanks its incorporation of dynamic response mechanisms for crop growth under stress factors. Overall, the novel and informative WHCrop model offers some advantages over traditional crop models since it allows for optimal decision making to be derived from the randomly different inputs. As a result, the WHCrop model proves instrumental in assisting decision-makers in formulating critical water allocation strategies and developing effective management recommendations to enhance regional agricultural water productivity. • WHCrop model simulates crop growth dynamics using water and heat drive. • WHCrop model enables randomly varied inputs for optimal decision-making. • WHCrop model simulates daily crop growth responses to water stress and climate changes. • WHCrop model needs fewer and readily available input data than traditional crop model. • WHCrop outperformed traditional crop models in simulating crop growth across scales. [ABSTRACT FROM AUTHOR]

Atmospheric longwave downward radiation (Ld) is one of the significant components of net radiation (Rn), and it drives several essential ecosystem processes. Ld can be estimated with simple empirical methods using atmospheric emissivity (εa) submodels. In this study, eight global models for εa were evaluated, and the best-performing model was calibrated on a global scale using a parametric instability analysis approach. The climatic data were obtained from a dynamically consistent scale resolution of basic atmospheric quantities and computed parameters known as NCEP/NCAR reanalysis (NNR) data. The performance model was evaluated with monthly average values from the NNR data. The Brutsaert equation demonstrated the best performance, and then it was calibrated. The seasonal global trend of the Brutsaert equation calibrated coefficient ranged between 1.2 and 1.4, and the K-means analysis identified five homogeneous zones (clusters) with similar behavior. Finally, the calibrated Brutsaert equation improved the Rn estimation, with an error reduction, at the worldwide scale, of 64%. Meanwhile, the error reduction for each cluster ranged from 18 to 77%. Hence, Brutsaert's equation coefficient should not be considered a constant value for use in εa estimation, nor in time or location. [ABSTRACT FROM AUTHOR]

There is an urgent need to ensure regional food security and increase irrigation water productivity in response to water shortages in arid and semi-arid regions. Previous studies of the optimal allocation of irrigation water did not consider simultaneously optimizing across multiple crops or at different growth stages. This paper describes the development of an irrigation water optimization model that uses a crop water allocation priority (CWAP) model. The CWAP value was determined by quantifying the changes in three indicators: yield, economic benefits, and irrigation water productivity. Maximum yield, maximum economic benefits, and minimum irrigation shortage (at the critical crop and growth stage) were used as the objective functions of a non-linear multi-objective optimization model. The largest irrigation district in the northern arid area of China, Hetao Irrigation District (HID), was chosen to prototype this model. The optimization results, using CWAP, showed that yield, economic benefits, irrigation water productivity, and water productivity could be increased, respectively, by up to 13.38%, 13.40%, 2.30%, and 6.29%, for most crops when compared with optimization results without CWAP. Comparison of the optimized net irrigation quantities with the actual net irrigation quantities showed that optimization reduced water usage by up to 60.77% for wheat, 51.24% for corn, and 63.59% for sunflower. Blue water utilization under optimal irrigation conditions decreased by 1.12% for wheat, 2.91% for corn, and 9.91% for sunflower, compared with those in actual irrigation scenario. This method of optimizing irrigation water allocation in arid areas using CWAP provides decision-makers with accurate water-saving irrigation protocols that will reduce demand for water resources and promote sustainable agriculture. [ABSTRACT FROM AUTHOR]

[Display omitted] • Classical optimal planting pattern (COPP) can improve regional water productivity. • The first assessment regarding the impact of COPP on food-energy-water-carbon nexus. • COPP can decrease carbon neutrality capacity by 13.09% in China's major arid area. • Need to redefine "optimum" planting pattern under the context of global warming. Global warming, water scarcity and limited land resources are the most challenging problems facing agriculture to ensure food security for the expected 9 billion people in 2050. To solve these problems, the classical optimal planting pattern, based on crop suitability evaluation method, is often adopted to reallocate water and land resources. However, whether or not the classical optimal planting pattern, which only considers environmental conditions in crop suitability evaluation, is beneficial to the regional carbon neutrality goal and saves water and energy resources has rarely been explored. Here, China's major arid food production area, the middle reaches of Heihe River Basin, is chosen as the demonstration to explore this issue. The classical optimal planting pattern obtained by crop suitability evaluation is compared with current planting in terms of planting distribution, carbon sequestration, energy consumption and water productivity from 2002 to 2016. Interestingly, the results indicate that optimal planting would reduce the regional net carbon sequestration capacity by up to 13.09% and increase regional carbon emissions by up to 22%, which is harmful to reach the commitment of carbon neutrality goal in China. Contrary, optimal crop planting pattern can increase regional water productivity by 1.74–32.59% and economic benefits by 1.52–30.55% while having little impact on energy consumption and water consumption. Considering the contradictions effects of classical optimal planting pattern on the food-energy-water-carbon nexus, we strongly recommend redefining the "optimal" in crop planting management by taking impacts on carbon into consideration to alleviate the crisis of global warming. [ABSTRACT FROM AUTHOR]

Drip irrigation combined with film mulching significantly improved water saving and agricultural efficiency in arid and semi-arid regions. However, the impact of various irrigation technical parameters under plastic film-mulched (PM) drip irrigation on water consumption and maize yield (Zea mays L.) for seed production is still unclear. Field experiments were conducted between 2016 and 2018 in Northwest China to investigate the effects of irrigation technical parameters (dripper discharge and irrigation interval) on biomass, grain yield, water, and nitrogen (N) use efficiency of the PM-maize for seed production. Four dripper discharges (1.38 L h−1, 2.0 L h−1, 2.5 L h−1, 3.0 L h−1) and five irrigation intervals (6 days, 8 days, 10 days, 12 days, 14 days) were selected. The normalized yield was used to analyze the effect of different treatments on the yield to eliminate the differences caused by uncontrollable factors, such as precipitation and temperature between different years. The results indicated a quadratic function among the final aboveground biomass, grain yield of maize for seed production, and irrigation intervals, with the highest value observed at 8 days irrigation interval. Both yield and nitrogen use efficiency measured as partial factor productivity of nitrogen (PFPN) decreased rapidly as the irrigation interval exceeded 10 days. When the irrigation interval increased from 10 to 14 days, the relative grain yield decreased between 6.5 and 16.0%, while the PFPN decreased between 1.1 and 99.2 kg kg−1. Crop evapotranspiration (ET) initially decreased and then increased as the irrigation interval increased. The water productivity (WPY-ET) was the highest at the irrigation interval of 8 days. The PFPN increased between 6.6 and 42.8 kg kg−1 at the dripper discharge of 3.0 L h−1 when compared with dripper discharge of 2.5 L h−1. The study showed an optimal dripper discharge–irrigation interval combination for maximizing water-resources utilization and yield increase. When biomass, grain yield, ET, and WPY-ET are considered simultaneously, the most suitable irrigation technical parameters of maize for seed production in Northwest China are the combination of 2.5 L h−1 and 8 days. Furthermore, a combination of 3.0 L h−1 and 8 days is more conducive to reducing fertilizer costs. [ABSTRACT FROM AUTHOR]

P1, P3, P5 are the irrigation intervals at 6, 10, 14 days, respectively. P5D are treatments with an irrigation interval of P5 The original article has been corrected. [Extracted from the article]

When planning agricultural production, planting area and water allocation are two major subjects faced by decision makers. In this study, a framework integrated Nerlove supply response model (Nerlove model) and interval fuzzy credibility-constraint bi-level programming (IFCBP) model is developed for planning the agricultural production in arid and semi-arid regions. Through Nerlove model, the planning process of crop planting area was described as an economic problem for forecasting farmers' behavior rather than an optimization problem for allocating farmland resources, and the relationship between crop planting area and market price can be obtained and further provide credible future crop planting area information. The IFCBP model can not only deal with uncertainties presented as interval and fuzzy numbers but also examine the credibility of the constraints and handle tradeoffs between two-level decision makers. To solve the IFCBP model, a solution method based on the interval interactive algorithm and credibility-cut method is proposed. Then, to verify the validity of the developed framework and solving method for agricultural production planning, they were applied to a real-case in the middle reaches of the Heihe River basin, northwest China. The forecasting results obtained from Nerlove model have better performance in predicting the future planting area of corn and vegetable than wheat, indicating that wheat plays a more vulnerable role in the decision-making process of planting area owing to its higher substitutability. The results show that the proposed framework can tackle two-level decision makers' concerns under uncertainties featured as inexact and fuzzy numbers, which can help regional managers plan future resources effectively. Furthermore, a comparison was made between IFCBP and two corresponding single-level models in this study. The comparison indicates that the developed model provides an effective tradeoff between two decision makers from different decision-making levels in IFCBP. The developed framework provides managers an effective way to plan agricultural production in arid and semi-arid regions, and the developed model and related thinking may help solve similar problems. • An agricultural production planning approach based on interval fuzzy credibility-constrained bi-level programming (IFCBP) and Nerlove supply response theory. • Both objective and subject factors are considered through the entropy method and analytic hierarchy process method separately. • This approach is applied to a case study for planning crop planting area and irrigation-water allocation. • The results can support water managers formulate more efficient agricultural production planning strategy. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel regional grid-scale crop dynamic layout optimization model was constructed. • Multiyear crop layout plan was obtained by considering inter-annual impacts. • Spatial variance of parameters and neighbourhood effects of crop units were noted. • Overall government planning and farmers' crop rotation behaviour were regarded. • Stakeholders and their multi-objective planting preferences were balanced. Rapid population growth has led to an increasing demand for food, accompanied by an increase in the resources invested in producing food, and the extensive food production practices have placed a huge burden on the environment. There is an urgent desire of need to find a sustainable model of food production. Therefore, this study attempted to develop a dynamic regional grid-scale crop layout model (ABCAMODP-Crop) combining agent-based model, multi-objective programming, dynamic programming and cellular automata model to promote more sustainable food production from a supply-side perspective. The model can: (1) take into account the planting preferences and practical behaviours of government and farmer as stakeholders in crop layout; (2) further weigh the conflicts among the various objectives that stakeholders are concerned with from resource, environmental, nutritional and economic dimensions; (3) consider the impact of the previous year's crop layout on the next year's crop placement; and (4) obtain grid-scale accurate crop layout solutions by considering the spatial variability of optimization parameters and the neighborhood effects of crop layout cells. The model was applied to the Shiyang River Basin in China to verify its usefulness. Over the entire planning period, crop layout schemes optimized with the objective of integrating irrigation water productivity, environmental impact, nutrient availability and economic benefits have higher comprehensive benefits than the original scheme, with an increase in average irrigation water productivity per unit area of 14.55%, an increase in total yield of 14.45%, an increase in total carbohydrate availability of 7.39%, an increase in total calcium availability of 18.74%, an increase in total net carbon sequestration of 8.05%, an increase in total net benefit of 20.08%. The model developed allows for a holistic view of crop layout and guides decision-makers in a rational distribution of regional crops, thus promoting more sustainable food production. It can equally be extended to other regions as a generic modelling framework. [ABSTRACT FROM AUTHOR]

Rapidly growing population has led to increasing food demands and greater food system pressure. It is urged to find a way to improve the sustainability of food system. Therefore, this study attempted to develop a regional crop layout model (CAMOO-Crop) combining cellular automata model with multi-objective optimization model to improve food system sustainability. The model can (1) consider the carbon sequestration of farmland vegetation, carbon emissions from production inputs, and human demand for crop nutrient supply, (2) balance the trade-offs among multiple dimensions of resource, environment, economy and nutrition in the crop food system, (3) obtain grid-scale crop space layout schemes considering special distribution. The model was applied to the Shiyang River Basin in China to verify its effectiveness. After optimization, crop water productivity per unit area increased by 2.12%, carbon sequestration increased by 0.32%, carbon emission decreased by 1.87%, the net benefit increased by 2.66%, carbohydrate supply decreased by 0.19%, calcium supply increased by 5.74%. In addition, a cropland-scale food system sustainability evaluation index system was constructed, and the Food System Sustainability Index (FSSI) and Coupling Coordination Degree (CCD) were introduced to evaluate the sustainability of food systems under different crop layout scenarios. The results showed that compared with single-objective model scenario and the status quo, CAMOO-Crop model performed best, with FSSI from 0.70 to 0.91; CCD from 0.83 to 0.95. The developed model can promote sustainable development of the regional food system and guide decision-makers to rationally distribute regional crops. [Display omitted] • A novel crop layout framework was developed to improve food system sustainability. • A model for coupling cellular automata and multi-objective programming was provided. • Trade-offs between resource, environment, economy and nutrition were realized. • A farmland scale food system sustainability evaluation system was constructed. • Layout options were developed for decision-makers considering special distribution. [ABSTRACT FROM AUTHOR]

Pharmacological therapies for cardiovascular diseases are limited by short-term pharmacokinetics and extra-cardiac adverse effects. Improving delivery selectivity specifically to the heart, wherein therapeutic drug levels can be maintained over time, is highly desirable. Nanoparticle (NP)-based pericardial drug delivery could provide a strategy to concentrate therapeutics within a unique, cardiac-restricted compartment to allow sustained drug penetration into the myocardium. Our objective was to explore the kinetics of myocardial penetration and retention after pericardial NP drug delivery. Fluorescently-tagged poly(lactic- co -glycolic acid) (PLGA) NPs were loaded with BODIPY, a fluorophore, and percutaneously administered into the pericardium via subxiphoid puncture in rabbits. At distinct timepoints hearts were examined for presence of NPs and BODIPY. PLGA NPs were found non-uniformly distributed on the epicardium following pericardial administration, displaying a half-life of ~ 2.5 days in the heart. While NPs were mostly confined to epicardial layers, BODIPY was capable of penetrating into the myocardium, resulting in a transmural gradient. The distinct architecture and physiology of the different regions of the heart influenced BODIPY distribution, with fluorophore penetrating more readily into atria than ventricles. BODIPY proved to have a long-term presence within the heart, with a half-life of ~ 7 days. Our findings demonstrate the potential of utilizing the pericardial space as a sustained drug-eluting reservoir through the application of nanoparticle-based drug delivery, opening several exciting avenues for selective and prolonged cardiac therapeutics. [ABSTRACT FROM AUTHOR]

Ineffective control over industrialization and urbanization has exposed the shortcomings of land resource management. The overexploitation of limited high-quality land resources is exacerbating the global ecological and food crisis. There is an urgent need to change from "expansion" to "intensification" in land resource utilization. Taking the northwest oasis region of China as a case study, a spatio-temporal optimization model of land use and land cover (LULC) based on dynamic competitiveness evaluation was constructed, and four simulation scenarios, including natural growth without policy intervention (SN), economic priority (SE), food security (SF) and ecological protection (SP), were set up to evaluate and predict the contribution of land intensification to regional carbon neutrality. The results show that the total carbon emissions in the study region will rise sharply by 157.56% in 2030 compared with 2020 without policy intervention (SN). In contrast, all three land intensification scenarios have great emission reduction potential: total regional carbon emissions can be reduced by 21.15% (SE), 22.78% (SF), and 51.72% (SP), respectively, compared with SN. Notably, the implementation of the ecological protection policy (SP) in the region will result in an average carbon emission per unit of GDP of 0.57 t/104 yuan in 2030, which is 25.44% lower than in 2020, implying a downward trend in carbon emissions per GDP between 2020 and 2030, and SP scenario will see the region achieve carbon peaking before 2030. The results can provide novelty methods and policy opinions for optimal land management from the perspective of a low-carbon city. [ABSTRACT FROM AUTHOR]

It is urgent to determine the crop critical irrigation period and the priority of crop water irrigation in arid and semi-arid regions to cope with agricultural water shortage. In this study, a method combining Agricultural Production Systems Simulator (APSIM) crop model with grey relational analysis was proposed to determine the irrigation priority of wheat, maize, and sunflower in the irrigation water distribution to ensure food security, and applied to the Hetao Irrigation District (HID) in China. Crop irrigation priority was determined by crop yield, water supply demand ratio (SDR), and water productivity (WP). The maximum yield, maximum water productivity and minimum water stress (crop growth stage) were evaluated. The obtained results indicate that: (1) When the irrigation scenario was 100%, the critical irrigation period for wheat was the first 10 days after sowing and 40–61 days after sowing; The critical irrigation priority for maize was 20–30 days after sowing and 61–81 days after sowing; And the critical irrigation priority for sunflower was 30–61 days after sowing. (2) In early May, irrigation priority was wheat priority to maize, while maize was a priority to wheat in mid to late May. In June, sunflower priority to wheat and maize irrigation. From early to middle July, irrigation priority to maize, and from late July to late August, sunflower water irrigation priority to maize. The method of to identify irrigation priority and crop priority to provide irrigation decision for allocating agriculture water resources. [Display omitted] • A novel method based on crop model and grey relational analysis is proposed. • The best irrigation time after sowing wheat, maize, and sunflower is identified. • Priority for intercrop water irrigation at the same time is proposed. • The effects of water stress, water productivity, and yield is considered. [ABSTRACT FROM AUTHOR]

Greenhouse gas emissions from irrigation and nitrogen fertilizer applications in agriculture are attracting increasing attention, and the development of low-carbon agriculture has become an inevitable trend in the implementation of carbon neutrality. However, the optimization of water and nitrogen in crop planting still lacks precise management at the grid scale. From the perspective of agricultural carbon emission reduction, this study proposes a precise regional water and nitrogen management approach that can optimize irrigation and nitrogen fertilizer application in space and time. In this study, a coupled crop water-nitrogen production function model was constructed by analyzing the response relationship between nitrogen and water on crop yield. The verification results showed that the model had superior fitting accuracy, with R2 and NRMSE values of 0.88, 10.50%, 0.8 and 7.76% for maize and wheat, respectively. After combining the water-nitrogen coupling function with the cellular automaton model, a spatiotemporal optimization model was developed to realize grid-scale regional water and nitrogen management. The case study showed that the optimized irrigation and nitrogen scheme could save 2.73% of agricultural water, decrease nitrogen fertilizer use by 9.69%, and reduce carbon emissions by 4.39%. Correlation analysis of irrigation and fertilization can provide accurate guidance for regional irrigation and fertilization systems. Therefore, considering the contribution of agricultural "carbon reduction sources" to carbon neutrality, water saving, and nitrogen reduction should be considered in low-carbon agricultural development, which is conducive to the green and sustainable development of agriculture. [Display omitted] • Established the response relationship between water-nitrogen and yield. • A grid-scale water-nitrogen coupling optimization model is developed. • Realized the spatiotemporal optimization of crop water and nitrogen. • Maintaining crop yield while water-saving and nitrogen-reducing. • Provided irrigation and nitrogen fertilization solutions for low carbon emission. [ABSTRACT FROM AUTHOR]

The pressures of frequent extreme weather, water shortages and increasing food demand pose a continued challenge of maintaining the sustainable development of irrigated agricultural systems. Although rational arable land management is fundamental to alleviating these pressures, the relationship between cropping patterns and irrigation sustainability is understudied. Using the Hexi Corridor as an example, a maximum entropy machine learning model was used to determine the optimal cropping pattern based on crop suitability and to explore the impact mechanism of the optimal cropping pattern on the irrigation sustainability index (SI) from the perspective of reliability, resilience, and vulnerability. An optimal cropping pattern was conducive to sustainable irrigation and reduced irrigation water use by 21.03% from 1960 s to 2010 s with no continued agricultural expansion. Thus, the challenges of food security and sustainability for similar regions, and globally, can be met but will require major changes in cropping planning and management. [Display omitted] • Crop suitability is an important basis for determining optimal cropping patterns. • Irrigation sustainability is assessed under optimal cropping patterns. • Adjusting the cropping pattern is an effective way to alleviate water scarcity. • Sustainable irrigation development needs more attention. [ABSTRACT FROM AUTHOR]

The orientation relationships between Cr precipitates and matrix in a Cu–Cr–Zr alloy with various aged states were investigated by transmission electron microscopy and High-resolution transmission electron microscopy, and the crystallographic transformations of orientation relationships were also analyzed by stereographic projection and discussed. Four kinds of orientation relationships were detected in the precipitation process of Cu–Cr–Zr alloy, and coexistence of Cr precipitates with Nishiyama-Wassermann relationship and Pitsch relationship was found in the peak-aged Cu–Cr–Zr alloy. The occurrence for cube-on-cube and Pitsch relationship in Cu–Cr–Zr alloy was thought to be caused by the retardation of additions and heterogeneous nucleation of Cr precipitates on dislocations respectively. Transformations of orientation relationships were observed during precipitation and the crystallographic rotation pathways of the transformations were represented by stereographic projection. The interrelationships of various orientation relationships were established by Bain strain and crystal rotation in the Cu–Cr–Zr alloy of this study. • Four kinds of ORs were detected during precipitation of Cu–Cr–Zr alloy. • Coexistence of N–W and Pitsch orientation was found in peak-aged Cu–Cr–Zr alloy. • Formation of Pitsch relationship may be related to heterogeneous nucleation. • Rotation pathways of ORs transformation are represented by stereographic projection. • Interrelationships of various ORs are established by Bain strain and crystal rotation. [ABSTRACT FROM AUTHOR]

Abstract: It is well known that tropical cyclones can cause upwelling, decrease of sea surface temperature, increase of chlorophyll-a (Chl-a) concentration and enhancement of primary production. But little is known about the response of dissolved oxygen (DO) concentration to a typhoon in the open ocean. This paper investigates the impact of a typhoon on DO concentration and related ecological parameters using in situ and remote sensing data. The in situ data were collected 1week after the passage of the super-typhoon Nanmadol in the northern South China Sea in 2011. An increase in DO concentration, accompanied by a decrease in water temperature and an increase in salinity and Chl-a concentration, was measured at sampling stations close to the typhoon track. At these stations, maximum DO concentration was found at a depth of around 5m and maximum Chl-a concentration at depths between 50 and 75m. The layer of high DO concentration extends from the surface to a depth of 35m and the concentrations stay almost constant down to this depth. Due to the passage of the typhoon, also a large sea level anomaly (21.6cm) and a high value of Ekman pumping velocity (4.0×10−4 ms−1) are observed, indicating upwelling phenomenon. At the same time, also intrusion of Kuroshio waters in the form of a loop current into the South China Sea (SCS) was observed. We attribute the increase of DO concentration after the passage of the typhoon to three effects: (1) entrainment of oxygen from the air into the upper water layer and strong vertical mixing of the water body due to the typhoon winds, (2) upwelling of cold nutrient-rich water which stimulates photosynthesis of phytoplankton and thus the generation of oxygen, which also increases the DO concentration due to cold water since the solubility of oxygen increase with decreasing water temperature, and, possibly, (3) transport of DO enriched waters from the Western Pacific to the SCS via the intrusion of Kuroshio waters. [Copyright &y& Elsevier]

Highlights: [•] An adjusted SW (MSW) model was proposed after considering mulching effect on ET. [•] The original SW model overestimated maize ET significantly over the whole season. [•] PM model performed well when canopy fully covered the substrate. [•] MSW model can simulate ET and Es well under film mulch without non-water stress. [ABSTRACT FROM AUTHOR]

Estimating water balance is the foundation of improving water productivity (WP) and managing crop production efficiently in fields with significant spatial variability of soil properties. Agricultural system models are useful tools to simulate crop yield, WP, and water balance; however, they are rarely focused on geostatistical characteristics on the spatial scale. If agricultural system models are used spatially, it is important to consider whether the geostatistical characteristics of the simulations are similar to those based on measured data. In this study, two process-based models, the widely-used Agricultural Production Systems sIMulator (APSIM) and the newly-developed soil Water Heat Carbon Nitrogen Simulator (WHCNS), were used to simulate crop yield, water balance, and WP in a 54-ha field with spatially variable soil properties. Performance of the simulations was good for both models; however, the simulation accuracy of WHCNS was higher than for APSIM. Geostatistical characteristics of measured maize yield and final soil water storage (SWS f) were different from the simulated data. The fitted semivariogram models of simulated yield and SWS f had a higher semivariogram range and lower random variation than that of measured data. The fitted semivariogram models and geostatistical characteristics of simulated water balance also varied between the two models. Although the agricultural system models simulated the spatial distribution of variables efficiently, their spatial structure was changed in comparison with the spatial structure of measured data. This would affect the interpolation precision of spatial distribution maps. More work is required on the robustness and prediction accuracy of both models for their implementation in a spatial way. • WHCNS was superior to APSIM for simulating yield and final soil water storage. • Spatial ranges for simulated yield and SWS f were larger than that of measurements. • Spatial dependence of simulated yield and SWS f were lower than that of measurements. • Geospatial characteristics of simulated water balance varied between the two models. [ABSTRACT FROM AUTHOR]

The finite water resources are the main factors limiting agricultural sustainable development in arid and semiarid zones. In these water-scarce areas, optimizing limited water resources is an important way to reduce water consumption, increase crop yields and improve regional economic benefits. In this study, a CA-Jensen optimization model integrating cellular automata method and crop water production function into multi-objective dynamic optimization was developed and applied to the middle reaches of the Heihe River basin in the Northwest China to adjust the spatial distribution of cropland and the spatiotemporal allocation of crop water consumption. The net economic benefits of unit water, the crop water productivity and the value of the terrestrial ecosystem services represented the economic, social and ecological benefits in the objective function of the optimization model respectively. Adaptive cellular genetic algorithm was utilized to search and solve the optimization model. The obtained optimization results of the base year (2015) showed that the net benefits of unit water increased by 4.5% and the crop water productivity increased by 28.6%. In addition, for the target year (2030), although the reduction of agricultural water and cultivated land by 12.95% and 18.63% respectively led to a decrease of total economic benefits by 15.72%, the net benefits of unit water and social benefits got increased by 15.71% and 20.52%, respectively. Therefore, the optimization model can provide decision makers with guidance on the rational allocation of regional agricultural water resources in the future. • Established a spatio-temporal dynamic optimization model of crop water consumption. • Provided a method coupling cellular automata with Jensen model on grid-scale. • The comprehensive benefits of irrigation are improved under water limitation. • Helping decision-makers implement water-adapted sustainable agricultural policies. [ABSTRACT FROM AUTHOR]

Background: The increasing prevalence of atrial fibrillation (AF) and chronic kidney diseases highlights the need for a deeper comprehension of the molecular mechanisms linking them. Mutations in PKD1, the gene encoding Polycystin-1 (PKD1 or PC1), account for 85% of autosomal dominant polycystic kidney disease (ADPKD) cases. This disease often includes cardiac complications such as AF. In cardiomyocytes, PC1 deletion reduces hypertrophic response to pressure overload but promotes baseline ventricular dysfunction, while deletion in fibroblasts ameliorates post-myocardial infarction fibrosis. Despite its known cardiac impact, the role of PC1 in atrial cardiomyocytes and arrhythmias is less understood. Here, we sought to investigate the role of PC1 in AF., Methods: We used intracardiac programmed stimulation and optical mapping to evaluate AF inducibility in two mouse models, Pkd1 R3277C, which recapitulates human ADPKD progression, and cardiomyocyte-specific Pkd1 deletion, and their respective controls. Isolated adult mouse atrial cardiomyocytes, human iPSC-derived atrial cardiomyocytes (hiPSC-aCM), and HL-1 cells served as in vitro cellular models. Molecular mechanisms were evaluated using optical mapping and molecular and biochemical approaches., Results: Loss-of-function PC1 mutations significantly increased AF susceptibility in vivo and facilitated local reentry in ex vivo left atrial appendages. Comprehensive in vitro experiments supported a direct effect of PC1 in atrial cardiomyocytes. PC1-deficient monolayers exhibited increased arrhythmic events, escalating into reentrant spiral waves post-tachypacing. Transcriptomics analysis revealed PC1-dependent regulation of DNA repair, with PC1 deficiency leading to increased DNA damage under stress. PARP1 inhibitors or nicotinamide riboside, which counteract DNA damage-related metabolic consequences, reduced in vitro arrhythmias PC1-deficient monolayers. Overexpression of the C-terminus of PC1 had the opposite effects in DNA repair genes, suggesting its regulatory effects in atrial cardiomyocytes through retinoblastoma/E2F. Analyses of human atrial tissue from non-ADPKD patients showed reduced levels of mature PC1, suggesting a broader relevance of impaired PC1 in AF., Conclusions: Impaired PC1 increases in vivo AF inducibility under programmed electrical stimulation and promotes in vitro arrhythmias in hiPSC-aCM and HL-1 cells. Our findings indicate that PC1 protects against DNA damage to reduce AF susceptibility.

Accurate assessment of evapotranspiration (ETa) and crop coefficient (Kc) is crucial for optimizing irrigation practices in water-scarce regions. While satellite-based surface energy balance models offer a promising solution, their application to sparse canopies like apple orchards requires specific validation. This study investigated the spatial and temporal dynamics of ETa and Kc in a drip-irrigated 'Pink Lady' apple orchard under Mediterranean conditions over three growing seasons (2012/13, 2013/14, 2014/15). The METRIC model, incorporating calibrated sub-models for leaf area index (LAI), surface roughness (Z om ), and soil heat flux (G), was employed to estimate ETa and Kc. These estimates were validated against field-scale Eddy Covariance data. Results indicated that METRIC overpredicted Kc and ETa with errors less than 10 %. These findings highlight the potential of the calibrated METRIC model as a valuable decision-making tool for irrigation management in apple orchards., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

In recent years, the sustainable development of agricultural water resources has received much attention. The mismatch between agricultural water distribution patterns, land resources, and socioeconomics threatens food production, especially in vast water-scarce plains. Therefore, monitoring agricultural water spatial equilibrium (AWRSE) is necessary. Based on equilibrium theory and information entropy, in this study, the AWRSE evaluation model is constructed from three aspects: agricultural water resources, land resources, and socioeconomics. In addition, the relationship between social factors with cropping pattern as the primary explanatory variable and AWRSE was examined in conjunction with the extended STIRPAT model and applied to the water-receiving area of the Middle Route of South-to-North Water Diversion Project (MR-SNWDP). The results show that compared with the pre-diversion period, the AWRSE of 75% of the water-receiving cities has been significantly improved by the MR-SNWTP water supply. The MK test z value of the overall regional AWRSE has changed from - 0.328 to - 2.65, and the AWRSE development has shifted from not significantly better to significantly better. The cropping pattern shows a positive response to this development, and this effect can be mitigated in the late stage of water transfer; when the proportion of food crop cultivation increases by 1%, the sub-regional AWRSE value will increase by 0.347%. The evaluation model demonstrates a broad range of inclusiveness and application potential; it provides novel insights for examining agroecological, social, and economic stability., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: The integrated health management system (IHMS), which unites all health care-related institutions under a health-centered organizational framework, is of great significance to China in promoting the hierarchical treatment system and improving the new health care reform. China's IHMS policy consists of multiple policies at different levels and at different times; however, there is a lack of comprehensive interpretation and analysis of these policies, which is not conducive to the further development of the IHMS in China., Objective: This study aims to comprehensively analyze and understand the characteristics, development, and evolution of China's IHMS policy to inform the design and improvement of the system., Methods: We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to collect 152 policy documents. With the perspective of policy tools and policy orientation as the core, a comprehensive 6D framework including policy level, policy nature, release time, policy tools, stakeholders, and policy orientation was established by combining the content of policy texts. These dimensions were then analyzed using content analysis., Results: First, we found that, regarding the coordination of policy tools and stakeholders, China's IHMS policy was more inclined to use environment-based policy tools (1089/1929, 56.45%), which suggests a need for further balance in the internal structure of policy tools. Attention to different actors varied, and the participation of physicians and residents needs further improvement (65/2019, 3.22% and 11/2019, 0.54%, respectively). Second, in terms of level differences, Shanghai's IHMS policy used fewer demand-based policy tools (43/483, 8.9%), whereas the national IHMS policy and those of other provinces and cities used fewer supply-based tools (61/357, 17.1% and 248/357, 69.5%, respectively). The national IHMS strategy placed more emphasis on the construction of smart health care (including digital health; 10/275, 3.6%), whereas Shanghai was a leader in the development of healthy community and healthy China (9/158, 5.7% and 4/158, 2.5%, respectively). Third, in terms of time evolution, the various policy tools showed an increasing and then decreasing trend from 2014 to 2021, with relatively more use of environment-based policy tools and less use of demand-based policy tools in the last 3 years. The growth of China's IHMS policy can be divided into 3 stages: the disease-centered period (2014-2017), the e-health technology development period (2017-2019), and the health-centered period (2018-2021)., Conclusions: Policy makers should make several adjustments, such as coordinating policy tools and the uneven relationships among stakeholders; grasping key policy priorities in the context of local characteristics; and focusing on horizontal, multidimensional integration of health resources starting from the community. This study expands the objects of policy research and improves the framework for policy analysis. The findings provide some possible lessons for future policy formulation and optimization., (©Yang Yu, Sufen Wang, Lijue You. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 24.01.2024.)

Background: This study aims to examine how big data resources affect the recall of prior medical knowledge by healthcare professionals, and how this differs in environments with and without remote consultation platforms., Method: This study investigated two distinct categories of medical institutions, namely 132 medical institutions with platforms, and 176 medical institutions without the platforms. Big data resources are categorized into two levels-medical institutional level and public level-and three types, namely data, technology, and services. The data are analyzed using SmartPLS2., Results: (1) In both scenarios, shared big data resources at the public level have a significant direct impact on the recall of prior medical knowledge. However, there is a significant difference in the direct impact of big data resources at the institutional level in both scenarios. (2) In institutions with platforms, for the three big data resources (the medical big data assets and big data deployment technical capacity at the medical institutional level, and policies of medical big data at the public level) without direct impacts, there exist three indirect pathways. (3) In institutions without platforms, for the two big data resources (the service capability and big data technical capacity at the medical institutional level) without direct impacts, there exist three indirect pathways., Conclusions: The different interactions between big data, technology, and services, as well as between different levels of big data resources, affect the way clinical doctors recall relevant medical knowledge. These interaction patterns vary between institutions with and without platforms. This study provides a reference for governments and institutions to design big data environments for improving clinical capabilities., (© 2023. The Author(s).)

The particulate backscattering coefficient (b bp ) plays an important role in the growth of coral reefs by influencing the light field conditions. Small-scale optically shallow waters are commonly found in coastal fringing reefs, making it challenging to monitor the spatial and temporal patterns accurately using Aqua satellites with a low spatial resolution. In this study, six existing optimization-based algorithms for deriving b bp at 400 nm (b bp (400)) were evaluated with three simulated Landsat-8 (spatial resolution = 30 m) data sets and in situ data from the Luhuitou Peninsula, Sanya. The comparison results indicated that the HOPE (hyperspectral optimization process exemplar) (Fix-H-error or Fix-H-error-free) algorithm which sets an input value of the water depth alone outperformed other algorithms. However, the estimated b bp (400) from all the algorithms tended to be either overestimated and underestimated due to the improper the spectral shape value of the backscattering coefficient. The HOPE (Fix-H-error) algorithm estimated-b bp (400) from in situ reflectance also had a good correlation with the in situ total suspended particle concentrations data derived-b bp (400), with a correlation coefficient of 0.83. Therefore, the HOPE (Fix-H-error) algorithm was selected to estimate the b bp (400) from satellite-based Landsat-8 data of the Luhuitou Peninsula, Sanya. Time-series (2014-2021) results from these Landsat-8 images reveal the seasonal variation of b bp (400). The b bp (400) was low from May to September every year. From October to December or January, b bp (400) had an increasing trend, and then it decreased until May. Spatial analysis indicated that b bp (400) decreased with increasing water depth. The spatial and temporal patterns of b bp (400) were consistent with in situ observations reported in the literature. This study preliminarily showed the efficiency of an optimization-based algorithm in deriving b bp (400) in small-scale optically shallow water region using Landsat-8 data.

In previous studies, adipocytes were found to play an important role in regulating whole-body nutrition and energy balance, and are also important in energy metabolism, hormone secretion, and immune regulation. Different adipocytes have different contributions to the body, with white adipocytes primarily storing energy and brown adipocytes producing heat. Recently discovered beige adipocytes, which have characteristics in between white and brown adipocytes, also have the potential to produce heat. Adipocytes interact with other cells in the microenvironment to promote blood vessel growth and immune and neural network interactions. Adipose tissue plays an important role in obesity, metabolic syndrome, and type 2 diabetes. Dysfunction in adipose tissue endocrine and immune regulation can cause and promote the occurrence and development of related diseases. Adipose tissue can also secrete multiple cytokines, which can interact with organs; however, previous studies have not comprehensively summarized the interaction between adipose tissue and other organs. This article reviews the effect of multi-organ crosstalk on the physiology and pathology of adipose tissue, including interactions between the central nervous system, heart, liver, skeletal muscle, and intestines, as well as the mechanisms of adipose tissue in the development of various diseases and its role in disease treatment. It emphasizes the importance of a deeper understanding of these mechanisms for the prevention and treatment of related diseases. Determining these mechanisms has enormous potential for identifying new targets for treating diabetes, metabolic disorders, and cardiovascular diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wang, Liu, Chen, He, Ma, Liu and Sun.)