Sustainable Intensification and Large-scale Operation of Cultivated Land Use at the Farmers’ Scale:A Case Study of Shandong Province,China

Sustainable intensification of cultivated land use(SICLU) and large-scale operations(LSO) are widely acknowledged strategies for enhancing agricultural performance.However,the existing literature has faced challenges in precisely defining SICLU and constructing comprehensive indicators,which has hindered the exploration of factors influencing LSO within the SICLU framework.To address this gap,we integrated self-efficacy theory into the design of an index framework for evaluating SICLU.We subsequently employed econometric models to analyze the significant factors that impact LSO.Our findings reveal that SICLU can be divided into four key dimensions:intensive management,efficient output,resource conservation,and ecological environment optimization.Furthermore,it is crucial to incorporate belief-based cognitive factors into the index system,as farmers’ understanding of fertilizer and pesticide application significantly influences their willingness to engage in LSO.Moreover,we identify grain market turnover as the most influential factor in promoting LSO,with single-factor contribution rates reaching 70.9% for cultivated land transfer willingness and 62.5% for the total planting areas.Interestingly,unlike irrigation and agricultural machinery inputs,increased labor inputs correspond to larger planting areas for farmers.This trend may be attributed to reduced labor availability because of rural labor migration,whereas the reduction in irrigation and agricultural input is contingent on innovations in production practices and the transfer of cultivated land management rights.Importantly,SICLU dynamically influences LSO,with each index related to SICLU having an optimal range that fosters LSO.These insights offer valuable guidance for policymakers,emphasizing farmers as their central focus,with the adjustment of input and output factors as a means to achieve LSO as the ultimate goal.In conclusion,we propose research avenues for further enriching the SICLU framework to ensure that it aligns with the specific characteristics of regional agricultural development.

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 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.

Large-scale model testing of high-pressure grouting reinforcement for bedding slope with rapid-setting polyurethane

Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining walls,stabilizing piles,and anchors,are time-consuming and labor-and energy-intensive.This study proposes an innovative polymer grout method to improve the bearing capacity and reduce the displacement of bedding slopes.A series of large-scale model tests were carried out to verify the effectiveness of polymer grout in protecting bedding slopes.Specifically,load-displacement relationships and failure patterns were analyzed for different testing slopes with various dosages of polymer.Results show the great potential of polymer grout in improving bearing capacity,reducing settlement,and protecting slopes from being crushed under shearing.The polymer-treated slopes remained structurally intact,while the untreated slope exhibited considerable damage when subjected to loads surpassing the bearing capacity.It is also found that polymer-cemented soils concentrate around the injection pipe,forming a fan-shaped sheet-like structure.This study proves the improvement of polymer grouting for bedding slope treatment and will contribute to the development of a fast method to protect bedding slopes from landslides.

Plasma nitrogen fixation system with dual-loop enhancement for improved energy efficiency and its efficacy for lettuce cultivation

Plasma nitrogen fixation(PNF)has been emerging as a promising technology for greenhouse gasfree and renewable energy-based agriculture.Yet,most PNF studies seldom address practical application-specific issues.In this work,we present the development of a compact and automatic PNF system for on-site agricultural applications.The system utilized a gliding-arc discharge as the plasma source and employed a dual-loop design to generate NO_(x)from air and water under atmospheric conditions.Experimental results showed that the system with a dualloop design performs well in terms of energy costs and production rates.Optimal operational parameters for the system were determined through experimentation,resulting in an energy cost of 13.9 MJ mol^(-1)and an energy efficiency of 16 g kWh^(-1)for NO_(3)^(-)production,respectively.Moreover,the concentration of exhausted NO_(x)was below the emission standards.Soilless lettuce cultivation experiments demonstrated that NO_(x)^(-)produced by the PNF system could serve as liquid nitrate nitrogen fertilizer.Overall,our work demonstrates the potential of the developed PNF system for on-site application in the production of green-leaf vegetables.

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.

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.

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.

Effects of water and nitrogen rate on grain-filling characteristics under high-low seedbed cultivation in winter wheat

A high-efficiency mode of high-low seedbed cultivation(HLSC)has been listed as the main agricultural technology to increase land utilization ratio and grain yield in Shandong Province,China.However,limited information is available on the optimized water and nitrogen management for yield formation,especially the grain-filling process,under HLSC mode.A three-year field experiment with four nitrogen rates and three irrigation rates of HLSC was conducted to reveal the response of grain-filling parameters,grain weight percentage of spike weight(GPS),spike moisture content(SMC),and winter wheat yield to water and nitrogen rates.The four nitrogen rates were N1(360 kg ha^(-1) pure N),N2(300 kg ha^(-1) pure N),N3(240 kg ha^(-1) pure N),and N4(180 kg ha^(-1) pure N),respectively,and the three irrigation quotas were W1(120 mm),W2(90 mm),and W3(60 mm),respectively.Results showed that the determinate growth function generally performed well in simulating the temporal dynamics of grain weight(0.989<R^(2)<0.999,where R2 is the determination coefficient).The occurrence time of maximum filling rate(T_(max))and active grain-filling period(AGP)increased with the increase in the water or nitrogen rate,whereas the average grain-filling rate(G_(mean))had a decreasing trend.The final 1,000-grain weight(FTGW)increased and then decreased with the increase in the nitrogen rates and increased with the increase in the irrigation rates.The GPS and SMC had a highly significant quadratic polynomial relationship with grain weight and days after anthesis.Nitrogen,irrigation,and year significantly affected the T_(max),AGP,G_(mean),and FTGW.Particularly,the AGP and FTGW were insignificantly different between high seedbed(HLSC-H)and low seedbed(HLSC-L)across the water and nitrogen levels.Moreover,the moderate water and nitrogen supply was more beneficial for grain yield,as well as for spike number and grain number per hectare.The principal component analysis indicated that combining 240-300 kg N ha^(-1) and 90^(-1)20 mm irrigation quota could improve grain-filling efficiency and yield for the HLSC-cultivated winter wheat.

Strawberry (Fragaria spp.): Cultivation, Production, Consumption, and Marketing in Cameroon

Strawberry (Fragaria spp.) is one of the most important fruits classified as exotic fruits imported into Cameroon. To have an inventory of its cultivation in Cameroon, a survey study was carried out among eight farms of Fragaria spp. from January 2021 to February 2022. The plant was introduced in Cameroon in 2018. There are 13 varieties of Fragaria spp. currently cultivated. Among these 13 varieties, eleven are hybrids of Fragaria x ananassa (“Amiga”, “Amine”, “Camarosa”, “Chandler”, “Charlotte”, “Elsanta”, “Gariguette”, “Madame Moutot”, “Ostara”, “Ruby gem” and “San Andreas”), and two of the hybrids of Fragaria vesca (“Maestro” and “Mara des bois”). The cropping system, irrigation system, and type of fertilizers applied differ from one strawberry farm to another. Biofertilizers (such as mycorrhizal), inorganic and organic fertilizers are actually used to improve production. The potential annual production of strawberries from January 2021 to February 2022, estimated based on the survey data, was 21.216 tons for all growers. Among these eight production farms, the Lolodorf BIO Farm presents 6000 kg (six tons) of strawberries and 100,000 stolons (seedlings) produced, from seven varieties of Fragaria spp. cultivated, with 6 varieties which are hybrids variety Fragaria x ananassa (“Amiga”, “Amine”, “Chandler”, “Gariguette”, “Madame Moutot”, and “Ruby gem”), and one which is a hybrid of Fragaria vesca (“Mara des bois”). Certain diseases were also observed and recorded depending on the growing areas.

Cultivation Path of Nursing Undergraduates’Scientific Research and Innovation Ability

Objective: The cultivation of the innovation ability and scientific research is one of the nursing learning objectives for undergraduate students. To explore the method and effect of training system of scientific research innovation ability of nursing undergraduates based on “3332”. Methods: Three course learning modules are constructed: stage-based course learning module, systematic project practice training module and comprehensive practice training module. A practical training platform for scientific research innovation projects is built, and undergraduate scientific research innovation ability training is carried out from both in-class and out-of-class lines. Results: Since 2017, the students have obtained 7 national innovation and entrepreneurship training programs, 52 university-level undergraduate scientific research projects, published more than 10 academic papers, and obtained 2 patent authorization. Conclusions: The training system of scientific research innovation ability of nursing undergraduates based on “3332” is conducive to the development of scientific research innovation ability of nursing students, and to cultivate nursing talents who can adapt to the development of the new era and have better post competence.

The response of roots and the rhizosphere environment to integrative cultivation practices in paddy rice

Integrative cultivation practices(ICPs)are essential for enhancing cereal yield and resource use efficiency.However,the effects of ICP on the rhizosphere environment and roots of paddy rice are still poorly understood.In this study,four rice varieties were produced in the field.Each variety was treated with six different cultivation techniques,including zero nitrogen application(0 N),local farmers’practice(LFP),nitrogen reduction(NR),and three progressive ICP techniques comprised of enhanced fertilizer N practice and increased plant density(ICP1),a treatment similar to ICP1 but with alternate wetting and moderate drying instead of continuous flooding(ICP2),and the same practices as ICP2 with the application of organic fertilizer(ICP3).The ICPs had greater grain production and nitrogen use efficiency than the other three methods.Root length,dry weight,root diameter,activity of root oxidation,root bleeding rate,zeatin and zeatin riboside compositions,and total organic acids in root exudates were elevated with the introduction of the successive cultivation practices.ICPs enhanced nitrate nitrogen,the activities of urease and invertase,and the diversity of microbes(bacteria)in rhizosphere and non-rhizosphere soil,while reducing the ammonium nitrogen content.The nutrient contents(ammonium nitrogen,total nitrogen,total potassium,total phosphorus,nitrate,and available phosphorus)and urease activity in rhizosphere soil were reduced in all treatments in comparison with the non-rhizosphere soil,but the invertase activity and bacterial diversity were greater.The main root morphology and physiology,and the ammonium nitrogen contents in rhizosphere soil at the primary stages were closely correlated with grain yield and internal nitrogen use efficiency.These findings suggest that the coordinated enhancement of the root system and the environment of the rhizosphere under integrative cultivation approaches may lead to higher rice production.

Detection of Bisphenol A (BPA) in Plastic Bottles Using Vertical Cultivation at Various Temperatures

Polycarbonate plastics containing bisphenol A (BPA) used to manufacture drinking water bottles. Kurdistan region in northern Iraq is a developed area with increased pollution from plastic bottles. Trace amounts of BPA have been detected in bottled water samples. The absorption of BPA was measured with HPLC using a vertical cultivation system with Bulbs of the Allium Cepa plant planted in these plastic bottles with monitored growth. Vertical cultivation was found to have a low level of BPA in the plant cells, making it a safe cultivation method under specific climate conditions. The mean concentration of BPA in vertical cultivation is 0.19 ug/ml (3.8 ng for a 20 uL injection), and the Limit of Quantification (LOQ) is 0.63 ug/ml (12.7 ng for 20 uL injection). While Scanning Electron Microscope (SEM) shows that the concentrations are relatively low in water samples stored at room temperature compared to those exposed to direct sunlight (40°C) and water bottle samples stored at (-4°C), The correlation coefficients were found to be good (0.9992). SEM is used for plastic bottle samples stored at different temperatures. The images identify compound decay and explore the morphology of BPA in manufactured plastic materials.

Determinants of Tobacco Cultivation and the Associated Impacts of Adoption in Hurungwe District, Zimbabwe

Tobacco is an essential cash crop in Zimbabwe and a strategic livelihood option for hundreds of thousands of rural households. However, the crop is linked to negative environmental, economic, and social impacts. The existing studies on tobacco cultivation in Zimbabwe present contradictory findings on the determinants and impacts of adoption, leaving unanswered questions about the crop’s sustainability impact in the country. This article investigates the determinants of smallholder farmers’ decisions to grow tobacco and the associated impacts of adoption. Random and purposive sampling were used to select 273 household surveys, including tobacco and non-tobacco smallholder farmers, and 56 expert interviews to answer the research questions. We employed regression models alongside expert interviews and document analysis to identify the determinants influencing the decision-making process of smallholder farmers in Zimbabwe regarding tobacco cultivation. Additionally, our investigation aimed to elucidate the perceived impacts associated with the adoption of this agricultural practice. The regression analysis indicated that the farmer’s age, education level, farming experience, family size, household income, and perceived high farm profitability are significant drivers of tobacco adoption. We also discovered divergent and convergent perceptions of the critical impacts of tobacco cultivation. The study highlights the need for proactive multi-stakeholder collaboration and sustainable financial arrangements to address the negative impacts of tobacco production. As the primary stakeholder responsible for regulating and promoting agricultural activities, the Zimbabwean government should provide meaningful financial support, increase access to credit, and ensure better market facilities for alternative crops to reduce the over-dependence on tobacco.

Strategic insights into the cultivation of pancreatic cancer organoids from endoscopic ultrasonography-guided biopsy tissue

BACKGROUND The frequent suboptimal efficacy of endoscopic ultrasound-guided fine-needle biopsy(EUS-FNB)to culture pancreatic cancer(PC)organoids(PCOs)poses a major challenge in the advancement of personalized medicine for advanced PC.AIM To explore how to obtain appropriate puncture tissues from EUS-FNB and optimize the strategy for efficiently constructing PCOs,providing an efficient tool for the advancement of personalized medicine.METHODS Patients who underwent EUS-FNB for the diagnosis of PC tissue were prospectively enrolled.We refined the endoscopic biopsy procedures and organoid cultivation techniques.All tissue specimens verified by on-site pathological assessment were cultured in a semi-suspended medium in a microfluidic environment.We assessed differences in PCOs cultured beyond and below five generations examining patient demographics,specimen and organoid attributes,and the sensitivity of organoids to a panel of clinical drugs through cell viability assays.RESULTS In this study,16 patients with PC were recruited,one sample was excluded because onsite cytopathology showed no tumor cells.Successful organoid generation occurred in 93.3%(14 of 15)of the EUS-FNB specimens,with 60%(9 of 15)sustaining over five generations.Among these patients,those with a history of diabetes,familial cancer,or larger tumors exhibited enhanced PCO expandability.The key factors influencing longterm PCOs expansion included initial needle sample quality(P=0.005),rapid initiation of organoid culture postisolation(P≤0.001),and high organoid activity(P=0.031).Drug sensitivity analysis revealed a partial response in two patients following therapeutic intervention and surgery and stable disease in four patients,indicating a moderate correlation between organoid response and clinical outcomes.CONCLUSION Optimal initial needle sampling,rapid and precise biopsy sample processing,process isolated samples as soon as possible,and sufficient cellular material are crucial for successful cultivating PCOs.High organoid activity is an important factor in maintaining their long-term expansion,which is essential for shortening the time of drug sensitivity analysis and is the basis of PC research.

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.

High Yield Cultivation and Pest Control Technology of Cashew

In order to enhance the yield and quality of cashew,it is essential to implement high-yield cultivation techniques effectively throughout the production process.Additionally,pest control measures should be employed to provide technical support for the industrialized development of cashew.

Factorized Smith Method for A Class of High-Ranked Large-Scale T-Stein Equations

We introduce a factorized Smith method(FSM)for solving large-scale highranked T-Stein equations within the banded-plus-low-rank structure framework.To effectively reduce both computational complexity and storage requirements,we develop techniques including deflation and shift,partial truncation and compression,as well as redesign the residual computation and termination condition.Numerical examples demonstrate that the FSM outperforms the Smith method implemented with a hierarchical HODLR structured toolkit in terms of CPU time.