Repositioning fertilizer manufacturing subsidies for improving food security and reducing greenhouse gas emissions in China

China removed fertilizer manufacturing subsidies from 2015 to 2018 to bolster market-oriented reforms and foster environmentally sustainable practices.However,the impact of this policy reform on food security and the environment remains inadequately evaluated.Moreover,although green and low-carbon technologies offer environmental advantages,their widespread adoption is hindered by prohibitively high costs.This study analyzes the impact of removing fertilizer manufacturing subsidies and explores the potential feasibility of redirecting fertilizer manufacturing subsidies to invest in the diffusion of these technologies.Utilizing the China Agricultural University Agri-food Systems model,we analyzed the potential for achieving mutually beneficial outcomes regarding food security and environmental sustainability.The findings indicate that removing fertilizer manufacturing subsidies has reduced greenhouse gas(GHG)emissions from agricultural activities by 3.88 million metric tons,with minimal impact on food production.Redirecting fertilizer manufacturing subsidies to invest in green and low-carbon technologies,including slow and controlled-release fertilizer,organic-inorganic compound fertilizers,and machine deep placement of fertilizer,emerges as a strategy to concurrently curtail GHG emissions,ensure food security,and secure robust economic returns.Finally,we propose a comprehensive set of government interventions,including subsidies,field guidance,and improved extension systems,to promote the widespread adoption of these technologies.

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

Effect of rice cultivar on greenhouse-gas emissions from rice-fish co-culture

In aquaculture,co-culturing rice with fish may mitigate greenhouse-gas emissions.In this study,co-culture of four rice cultivars in a laboratory-scale rice–fish system reduced CH_(4)and N_(2)O emissions relative to fish monoculture.Differences in CH_(4)and N_(2)O emissions among rice cultivars primarily stem from the differential effects of rice plants on plant-mediated CH_(4)transport,CH_(4)oxidation and nitrogen absorption.

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Technique of Earthworms Restoring Soil in Greenhouse Cultivation

The production environment of greenhouse cultivation is relatively closed,the multiple cropping index is high,the management of fertilizationwatering and pesticideapplication isblindtosomeextent,andthe phenomenonofcontinuous cropping isalsocommonSoilquali-ty affects the sustainable development of greenhouse cultivation.Earthworm is a ubiquitous invertebrate organism in soil,an important part of soil system,a link between terrestrial organisms and soil organisms,an important link in the small cycle of soil material organisms,and plays an important role in maintaining the structure and function of soil ecosystem.Different ecotypes of earthworms are closely related to their habi-tats(soil layers)and food resource preferences,and then affect their ecological functions.The principle of earthworm regulating soil function is essentially the close connection and interaction between earthworm and soil microorganism.Using different ecotypes of earthworms and bio-logical agents to carry out combined remediation of greenhouse cultivation soil is a technical model to realize sustainable development of green-house cultivation.

Effects of long-term partial substitution of inorganic fertilizer with pig manure and/or straw on nitrogen fractions and microbiological properties in greenhouse vegetable soils

Partial substitution of inorganic fertilizers with organic amendments is an important agricultural management practice.An 11-year field experiment(22 cropping periods)was carried out to analyze the impacts of different partial substitution treatments on crop yields and the transformation of nitrogen fractions in greenhouse vegetable soil.Four treatments with equal N,P_(2)O_(5),and K_(2)O inputs were selected,including complete inorganic fertilizer N(CN),50%inorganic fertilizer N plus 50%pig manure N(CPN),50%inorganic fertilizer N plus 25%pig manure N and 25%corn straw N(CPSN),and 50%inorganic fertilizer N plus 50%corn straw N(CSN).Organic substitution treatments tended to increase crop yields since the 6th cropping period compared to the CN treatment.From the 8th to the 22nd cropping periods,the highest yields were observed in the CPSN treatment where yields were 7.5-11.1%greater than in CN treatment.After 11-year fertilization,compared to CN,organic substitution treatments significantly increased the concentrations of NO_(3)^(-)-N,NH_(4)^(+)-N,acid hydrolysis ammonium-N(AHAN),amino acid-N(AAN),amino sugar-N(ASN),and acid hydrolysis unknown-N(AHUN)in soil by 45.0-69.4,32.8-58.1,49.3-66.6,62.0-69.5,34.5-100.3,and 109.2-172.9%,respectively.Redundancy analysis indicated that soil C/N and OC concentration significantly affected the distribution of N fractions.The highest concentrations of NO_(3)^(-)-N,AHAN,AAN,AHUN were found in the CPSN treatment.Organic substitution treatments increased the activities ofβ-glucosidase,β-cellobiosidase,N-acetyl-glucosamidase,L-aminopeptidase,and phosphatase in the soil.Organic substitution treatments reduced vector length and increased vector angle,indicating alleviation of constraints of C and N on soil microorganisms.Organic substitution treatments increased the total concentrations of phospholipid fatty acids(PLFAs)in the soil by 109.9-205.3%,and increased the relative abundance of G^(+)bacteria and fungi taxa,but decreased the relative abundance of G-bacteria,total bacteria,and actinomycetes.Overall,long-term organic substitution management increased soil OC concentration,C/N,and the microbial population,the latter in turn positively influenced soil enzyme activity.Enhanced microorganism numbers and enzyme activity enhanced soil N sequestration by transforming inorganic N to acid hydrolysis-N(AHN),and enhanced soil N supply capacity by activating non-acid hydrolysis-N(NAHN)to AHN,thus improving vegetable yield.Application of inorganic fertilizer,manure,and straw was a more effective fertilization model for achieving sustainable greenhouse vegetable production than application of inorganic fertilizer alone.

Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

Microplastic Impacts on Greenhouse Gases Emissions in Terrestrial Ecosystems

Microplastics can influence global climate change by regulating the emissions of greenhouse gases from different ecosystems. The effects of microplastics in terrestrial ecosystems are still not well studied particularly greenhouse gases emissions. Thus, we conducted a laboratory experiment over a period of 90 days with two types of microplastics (differing in their chemical structure), high density polyethylene (HDPE) and low density polyethylene (LDPE), which were applied to the soil at a rate of 0% to 0.1% (w/w). The overarching aim was to investigate the effects of microplastic type, microplastic concentration and days of exposure on greenhouse gases emissions. We also used original and artificially weathered microplastics (the same HDPE and LDPE) to make a comparison of greenhouse gases emissions between the original microplastics treated soils and the soils treated with weathered microplastics. Our findings showed that HDPE and LDPE microplastics significantly increased the emissions of greenhouse gases from the soil than that of the control soils. Emissions were increased with the increases in the level of microplastic in the soil. The weathered microplastic emitted greater quantity of greenhouse gases compared to that of the original microplastics. In contrast to a low initial emission quantity, the emissions were gradually increased at the termination of the experiment. Our experiment on the emissions of greenhouse gases from the soil vis-à-vis microplastic additions indicated that the microplastic increased the emissions of greenhouse gases in terrestrial ecosystems, and pervasive microplastic impacts may have consequences for the global climate change. Greenhouse gases emissions from the soil not only depend on the type and concentration of the microplastic, but also on the days of exposure to the microplastic.

Numerical and theoretical study of large-scale failure of strata overlying sublevel caving mines with steeply dipping discontinuities

The deformation and fracture evolution mechanisms of the strata overlying mines mined using sublevel caving were studied via numerical simulations.Moreover,an expression for the normal force acting on the side face of a steeply dipping superimposed cantilever beam in the surrounding rock was deduced based on limit equilibrium theory.The results show the following:(1)surface displacement above metal mines with steeply dipping discontinuities shows significant step characteristics,and(2)the behavior of the strata as they fail exhibits superimposition characteristics.Generally,failure first occurs in certain superimposed strata slightly far from the goaf.Subsequently,with the constant downward excavation of the orebody,the superimposed strata become damaged both upwards away from and downwards toward the goaf.This process continues until the deep part of the steeply dipping superimposed strata forms a large-scale deep fracture plane that connects with the goaf.The deep fracture plane generally makes an angle of 12°-20°with the normal to the steeply dipping discontinuities.The effect of the constant outward transfer of strata movement due to the constant outward failure of the superimposed strata in the metal mines with steeply dipping discontinuities causes the scope of the strata movement in these mines to be larger than expected.The strata in the metal mines with steeply dipping discontinuities mainly show flexural toppling failure.However,the steeply dipping structural strata near the goaf mainly exhibit shear slipping failure,in which case the mechanical model used to describe them can be simplified by treating them as steeply dipping superimposed cantilever beams.By taking the steeply dipping superimposed cantilever beam that first experiences failure as the key stratum,the failure scope of the strata(and criteria for the stability of metal mines with steeply dipping discontinuities mined using sublevel caving)can be obtained via iterative computations from the key stratum,moving downward toward and upwards away from the goaf.

Collapse Behavior of Pipe-Framed Greenhouses with and without Reinforcement under Snow Loading:A 3-D Finite Element Analysis

The present paper first investigates the collapse behavior of a conventional pipe-framed greenhouse under snow loading based on a 3-D finite element analysis,in which both geometrical and material non-linearities are considered.Three snow load distribution patterns related to the wind-driven snow particle movement are used in the analysis.It is found that snow load distribution affects the deformation and collapse behavior of the pipe-framed greenhouse significantly.The results obtained in this study are consistent with the actual damage observed.Next,discussion is made of the effects of reinforcements by adding members to the basic frame on the strength of the whole structure,in which seven kinds of reinforcement methods are examined.A buckling analysis is also carried out.The results indicate that the most effective reinforcement method depends on the snow load distribution pattern.

Financial Calculation Problems and Countermeasure Analysis of Large-Scale Engineering Construction Projects

The financial aspects of large-scale engineering construction projects profoundly influence their success.Strengthening cost control and establishing a scientific financial evaluation system can enhance the project’s economic benefits,minimize unnecessary costs,and provide decision-makers with a robust financial foundation.Additionally,implementing an effective cash flow control mechanism and conducting a comprehensive assessment of potential project risks can ensure financial stability and mitigate the risk of fund shortages.Developing a practical and feasible fundraising plan,along with stringent fund management practices,can prevent fund wastage and optimize fund utilization efficiency.These measures not only facilitate smooth project progression and improve project management efficiency but also enhance the project’s economic and social outcomes.

Gleaning insights from German energy transition and large-scale underground energy storage for China’s carbon neutrality

The global energy transition is a widespread phenomenon that requires international exchange of experiences and mutual learning.Germany’s success in its first phase of energy transition can be attributed to its adoption of smart energy technology and implementation of electricity futures and spot marketization,which enabled the achievement of multiple energy spatial–temporal complementarities and overall grid balance through energy conversion and reconversion technologies.While China can draw from Germany’s experience to inform its own energy transition efforts,its 11-fold higher annual electricity consumption requires a distinct approach.We recommend a clean energy system based on smart sector coupling(ENSYSCO)as a suitable pathway for achieving sustainable energy in China,given that renewable energy is expected to guarantee 85%of China’s energy production by 2060,requiring significant future electricity storage capacity.Nonetheless,renewable energy storage remains a significant challenge.We propose four large-scale underground energy storage methods based on ENSYSCO to address this challenge,while considering China’s national conditions.These proposals have culminated in pilot projects for large-scale underground energy storage in China,which we believe is a necessary choice for achieving carbon neutrality in China and enabling efficient and safe grid integration of renewable energy within the framework of ENSYSCO.

Modular Extremely Large-Scale Array Communication:Near-Field Modelling and Performance Analysis

This paper investigates the wireless communication with a novel architecture of antenna arrays,termed modular extremely large-scale array(XLarray),where array elements of an extremely large number/size are regularly mounted on a shared platform with both horizontally and vertically interlaced modules.Each module consists of a moderate/flexible number of array elements with the inter-element distance typically in the order of the signal wavelength,while different modules are separated by the relatively large inter-module distance for convenience of practical deployment.By accurately modelling the signal amplitudes and phases,as well as projected apertures across all modular elements,we analyse the near-field signal-to-noise ratio(SNR)performance for modular XL-array communications.Based on the non-uniform spherical wave(NUSW)modelling,the closed-form SNR expression is derived in terms of key system parameters,such as the overall modular array size,distances of adjacent modules along all dimensions,and the user's three-dimensional(3D)location.In addition,with the number of modules in different dimensions increasing infinitely,the asymptotic SNR scaling laws are revealed.Furthermore,we show that our proposed near-field modelling and performance analysis include the results for existing array architectures/modelling as special cases,e.g.,the collocated XL-array architecture,the uniform plane wave(UPW)based far-field modelling,and the modular extremely large-scale uniform linear array(XL-ULA)of onedimension.Extensive simulation results are presented to validate our findings.

Feeding the world: impacts of elevated [CO_(2)] on nutrient content of greenhouse grown fruit crops and options for future yield gains

Several long-term studies have provided strong support demonstrating that growing crops under elevated[CO_(2)]can increase photosynthesis and result in an increase in yield,flavour and nutritional content(including but not limited to Vitamins C,E and pro-vitamin A).In the case of tomato,increases in yield by as much as 80%are observed when plants are cultivated at 1000 ppm[CO_(2)],which is consistent with current commercial greenhouse productionmethods in the tomato fruit industry.These results provide a clear demonstration of the potential for elevating[CO_(2)]for improving yield and quality in greenhouse crops.The major focus of this review is to bring together 50 years of observations evaluating the impact of elevated[CO_(2)]on fruit yield and fruit nutritional quality.In the final section,we consider the need to engineer improvements to photosynthesis and nitrogen assimilation to allow plants to take greater advantage of elevated CO_(2) growth conditions.