Quantification of CO_(2) Emissions from Three Power Plants in China Using OCO-3 Satellite Measurements

Coal-fired power plants are a major carbon source in China. In order to assess the evaluation of China's carbon reduction progress with the promise made on the Paris Agreement, it is crucial to monitor the carbon flux intensity from coal-fired power plants. Previous studies have calculated CO_(2) emissions from point sources based on Orbiting Carbon Observatory-2 and-3(OCO-2 and OCO-3) satellite measurements, but the factors affecting CO_(2) flux estimations are uncertain. In this study, we employ a Gaussian Plume Model to estimate CO_(2) emissions from three power plants in China based on OCO-3 XCO_(2) measurements. Moreover, flux uncertainties resulting from wind information, background values,satellite CO_(2) measurements, and atmospheric stability are discussed. This study highlights the CO_(2) flux uncertainty derived from the satellite measurements. Finally, satellite-based CO_(2) emission estimates are compared to bottom-up inventories.The satellite-based CO_(2) emission estimates at the Tuoketuo and Nongliushi power plants are ~30 and ~10 kt d^(-1) smaller than the Open-Data Inventory for Anthropogenic Carbon dioxide(ODIAC) respectively, but ~10 kt d^(-1) larger than the ODIAC at Baotou.

Dominant woody plant species recognition with a hierarchical model based on multimodal geospatial data for subtropical forests

Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.

Choice of the Best Production Prediction Model for the Zagtouli Solar Power Plant in Burkina-Faso

In this paper, we present a study on the prediction of the power produced by the 33 MWp photovoltaic power plant at Zagtouli in Burkina-Faso, as a function of climatic factors. We identified models in the literature, namely the Benchmark, input/output, Marion, Cristo-fri, Kroposki, Jones-Underwood and Hatziargyriou prediction models, which depend exclusively on environmental parameters. We then compared our linear model with these seven mathematical models in order to determine the most optimal prediction model. Our results show that the Hatziargyriou model is better in terms of accuracy for power prediction.

Analysis of Genetic Effects of Nuclear-Cytoplasmic Interaction on Quantitative Traits:Genetic Model for Diploid Plants

A genetic model was proposed for simultaneously analyzing genetic effects of nuclear, cytoplasm, and nuclear-cytoplasmic interaction （NCI） as well as their genotype by environment （GE） interaction for quantitative traits of diploid plants. In the model, the NCI effects were further partitioned into additive and dominance nuclear-cytoplasmic interaction components. Mixed linear model approaches were used for statistical analysis. On the basis of diallel cross designs, Monte Carlo simulations showed that the genetic model was robust for estimating variance components under several situations without specific effects. Random genetic effects were predicted by an adjusted unbiased prediction （AUP） method. Data on four quantitative traits （boll number, lint percentage, fiber length, and micronaire） in Upland cotton （Gossypium hirsutum L.） were analyzed as a worked example to show the effectiveness of the model.

Genetic control of flowering time in woody plants： Roses as an emerging model

Genetic control of the timing of flowering in woody plants is complex and has yet to be adequately investigated due to their long life-cycle and difficulties in genetic modification.Studies in Populus,one of the best woody plant models,have revealed a highly conserved genetic network for flowering timing in annuals.However,traits like continuous flowering cannot be addressed with Populus.Roses and strawberries have relatively small,diploid genomes and feature enormous natural variation.With the development of new genetic populations and genomic tools,roses and strawberries have become good models for studying the molecular mechanisms underpinning the regulation of flowering in woody plants.Here,we review findings on the molecular and genetic factors controlling continuous flowering in roses and woodland strawberries.Natural variation at TFL1 orthologous genes in both roses and strawberries seems be the key plausible factor that regulates continuous flowering.However,recent efforts suggest that a two-recessive-loci model may explain the controlling of continuous flowering in roses.We propose that epigenetic factors,including non-coding RNAs or chromatin-related factors,might also play a role.Insights into the genetic control of flowering time variation in roses should benefit the development of new germplasm for woody crops and shed light on the molecular genetic bases for the production and maintenance of plant biodiversity.

Trait-based representation of hydrological functional properties of plants in weather and ecosystem models

Land surface models and dynamic global vegetation models typically represent vegetation through coarse plant functional type groupings based on leaf form, phenology, and bioclimatic limits. Although these groupings were both feasible and functional for early model generations, in light of the pace at which our knowledge of functional ecology, ecosystem demographics, and vegetation-climate feedbacks has advanced and the ever growing demand for enhanced model performance, these groupings have become antiquated and are identified as a key source of model uncertainty. The newest wave of model development is centered on shifting the vegetation paradigm away from plant functional types(PFTs)and towards flexible trait-based representations. These models seek to improve errors in ecosystem fluxes that result from information loss due to over-aggregation of dissimilar species into the same functional class. We advocate the importance of the inclusion of plant hydraulic trait representation within the new paradigm through a framework of the whole-plant hydraulic strategy. Plant hydraulic strategy is known to play a critical role in the regulation of stomatal conductance and thus transpiration and latent heat flux. It is typical that coexisting plants employ opposing hydraulic strategies, and therefore have disparate patterns of water acquisition and use. Hydraulic traits are deterministic of drought resilience, response to disturbance, and other demographic processes. The addition of plant hydraulic properties in models may not only improve the simulation of carbon and water fluxes but also vegetation population distributions.

Robust Stability of Nonlinear Plants with a Non-symmetric Prandtl-Ishlinskii Hysteresis Model

In this paper, robust stability of nonlinear plants represented by non-symmetric Prandtl-Ishlinskii （PI） hysteresis model is studied. In general, PI hysteresis model is the weighted superposition of play or stop hysteresis operators, and the slopes of the operators are considered to be the same. In order to make a hysteresis model, a modified form of non-symmetric play hysteresis operator with unknown slopes is given. The hysteresis model is described by a generalized Lipschitz operator term and a bounded parasitic term. Since the generalized Lipschitz operator is unknown, a new condition using robust right coprime factorization is proposed to guarantee robust stability of the controlled plant with the hysteresis nonlinearity. As a result, based on the proposed robust condition, a stabilized plant is obtained. A numerical example is presented to validate the effectiveness of the proposed method.

Tomato detection method using domain adaptive learning for dense planting environments

This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.

Business Model Analysis of Shunxiang Hazelnut Planting Cooperative Using the Business Model Canvas

Agricultural cooperatives serve as typical entities within the agricultural sector of China and encounter common representative challenges during their growth.This study employs the business model canvas theory to analyze the profit and business models of the Shunxiang Hazelnut Planting Cooperative while clarifying its ongoing developmental hurdles.By addressing pivotal concerns the paper puts forth branding suggestions and outlines future developmental directions.

Load Frequency Control of Small Hydropower Plants Using One-Input Fuzzy PI Controller with Linear and Non-Linear Plant Model

This study presents an intelligent approach for load frequency control (LFC) of small hydropower plants (SHPs). The approach which is based on fuzzy logic (FL), takes into account the non-linearity of SHPs—something which is not possible using traditional controllers. Most intelligent methods use two- input fuzzy controllers, but because such controllers are expensive, there is economic interest in the relatively cheaper single-input controllers. A non- linear control model based on one-input fuzzy logic PI (FLPI) controller was developed and applied to control the non-linear SHP. Using MATLAB/Si- mulink SimScape, the SHP was simulated with linear and non-linear plant models. The performance of the FLPI controller was investigated and compared with that of the conventional PI/PID controller. Results show that the settling time for the FLPI controller is about 8 times shorter;while the overshoot is about 15 times smaller compared to the conventional PI/PID controller. Therefore, the FLPI controller performs better than the conventional PI/PID controller not only in meeting the LFC control objective but also in ensuring increased dynamic stability of SHPs.

Effect of dry-wet cycles on dynamic properties and microstructures of sandstone:Experiments and modelling

Underground pumped storage power plant(UPSP)is an innovative concept for space recycling of abandoned mines.Its realization requires better understanding of the dynamic performance and durability of reservoir rock.This paper conducted ultrasonic detection,split Hopkinson pressure bar(SHPB)impact,mercury intrusion porosimetry(MIP),and backscatter electron observation(BSE)tests to investigate the dynamical behaviour and microstructure of sandstone with cyclical dry-wet damage.A coupling FEM-DEM model was constructed for reappearing mesoscopic structure damage.The results show that dry-wet cycles decrease the dynamic compressive strength(DCS)with a maximum reduction of 39.40%,the elastic limit strength is reduced from 41.75 to 25.62 MPa.The sieved fragments obtain the highest crack growth rate during the 23rd dry-wet cycle with a predictable life of 25 cycles for each rock particle.The pore fractal features of the macropores and micro-meso pores show great differences between the early and late cycles,which verifies the computational statistics analysis of particle deterioration.The numerical results show that the failure patterns are governed by the strain in pre-peak stage and the shear cracks are dominant.The dry-wet cycles reduce the energy transfer efficiency and lead to the discretization of force chain and crack fields.

Research on Light Response Curve Fitting Model of Four Chamaenerion Plants on the Serzilla Mountains

In order to study the applicability of different light response models to the photoresponse curves of four species of Chamaenerion, four species of Chamaenerion collected from Serzilla were used as test materials. Four common photosynthetic models were used to fit the photosynthetic response curve of the leaves. The results show that: 1) The effect of different photosynthetic response models on photosynthetic response curve of the genus chromasia was different. The fitted value of the correction model of right angled hyperbola was closest to the measured value, the R2 was 0.998, and RE was 0.216. 2) In terms of fitting the photosynthetic parameters, the initial quantum efficiency, light compensation point and dark respiration rate were suitable for fitting with non-right angle hyperbolic model. In terms of fitting the photosynthetic parameters, the initial quantum efficiency, light compensation point and dark respiration rate were suitable for fitting with non-right angle hyperbolic model. 3) The photosynthetic characteristics of C. angustifolium subsp. circumvagum reflect the negative response to high altitude radiation. Under strong radiation, the photosynthetic rate, apparent quantum efficiency, and light saturation point are low. On the other hand, C. conspersum has a positive response to high altitude radiation. C. angustifolium and C. latifolium has a higher light compensation point and higher light saturation point, but it is also positive for high altitude radiation. The response is only a choice of light intensity at different elevations, which also explains the distribution of C. angustifolium and C. latifolium in the salmonella with a single elevation, habitat, and slope.

Application of the BASNEF Model in Safety Training in Automobile Manufacturing Plants

After controls,including engineering and management,thefinal way to control noise is to use hearing protection devices.Duetothelack ofastandardized questionnaireregarding investigatingworkers’use of hearing protection devices on the basis of the BASNEF behavioral model,the present study was conducted to investigate the effect of health education based on the BASNEF model on the use of hearing protection devices in workers of an auto-mobile manufacturing plant in Iran.This quasi-experimental and prospective intervention study was performed on 80 workers at an automobile manufacturing plant who are exposed to noise levels above 85 decibels and donot use hearing protection devices.In this study,40 people working in a cast iron foundry were selected as the inter-vention group,and 40 working in aluminum casting were chosen as the control group.Questionnaires were ana-lyzed at the beginning of the intervention and three months after the intervention in the intervention and control groups.There was no significant difference between the intervention and control groups before the intervention.There was a significant difference between the intervention and control groups after the intervention in the area of knowledge.A significant correlation was observed between the intervention and control groups after the inter-vention program in all areas of the BASNEF educational model except behavioral intention.In this study,the effect of educational intervention on the use of hearing protection devices was investigated,and with the educa-tional intervention,it was tried to get help from influential people and enabling factors in education based on the BASNEF model.The results showed that the educational intervention based on the BASNEF model can improve the knowledge of individuals in both the intervention and control groups.

Prediction and Analysis of Total Nitrogen in a Sewage Treatment Plant Effluent

Total nitrogen was an important indicator for characterizing eutrophication of polluted water. Although the use of water quality online monitoring instrument can monitor water quality changes in real time, the degree of intelligence was low, so it was urgent to predict the water quality and take precautions in advance. A predictive model for total nitrogen levels in a sewage treatment plant utilizing the Anaerobic-Anoxic-Oxic (AAO) process was investigated in this paper. This model demonstrated significant practical application value. Based on the ARIMA (Autoregressive Integrated Moving Average) model and taking into account the impact of Biochemical Oxygen Demand (BOD), a prediction model for effluent total nitrogen was developed. However, the initial results exhibited significant deviations. To address this issue, seasonal factors were further considered. Then, the dataset was divided into winter and Non-winter sub-samples, leading to a reconstruction of the prediction model. Additionally, in developing the Non-winter prediction model, life cycle considerations were incorporated, and consequently, a SARIMA (Seasonal Autoregressive Integrated Moving Average) model was established. The predicting deviation associated with both the winter and Non-winter forecasting models showed a significant reduction.

Primary Study of Power-Exchanging Trans- action Model Among Price-Varied Power Plants in Practical Conditions

At present, electricity price to grid of domestic power plants is priced by the national administration based on the policy of "one power plant with one electricity price to grid," which is difficult to realize real bidding for access to grid in practice in a short term. This paper presents one kind of power-exchanging transaction model among price-varied power plants, which will be beneficial to price-varied power plants without any loss of profits of them and guarantee state-owned assets profits in minimum loss with no promotion of average price limit by power plants. Under ideal conditions, the computation results showed the sufficiency and necessity of power-exchanging transaction and maximum similarity with the requirements of optimized resources disposition in economics. The presented model is shown to be full of practicability and has been used in some part of power market.

Construction of Early-warning Model for Plant Diseases and Pests Based on Improved Neural Network

By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant diseases based on particle swarm and neural network algorithm was established. The test results showed that the construction of early-warning model is effective and feasible, which will provide a via- ble model structure to establish the effective early-warning platform.

Virtual Plant Modeling Based on Mutual Feedback of Function-structure

[Objective] To study virtual plant modeling based on mutual feedback of function-structure.[Method] With the analysis of the shortcomings of current virtual plant modeling method,the modeling with the idea of function-structure mutual feedback was put forward,and the steps of this modeling were elaborated,including the determination of morphological structure model,biomass production model,biomass allocation model,organ reconstruction model,and the integration method of function model and morphological structure model.[Results] The breakthrough of function-structure mutual feedback based mechanism from the boundaries of physiological ecology model and morphological structure model can solve the difficulty of data transmission between the two models and build an integrated model from the two,which can effectively reflect the incidence relation between plant morphology and function,and more suitable for the growth mechanisms of plants.This modeling approach has significant advantages in the dynamic simulation of plant growth.[Conclusion] The virtual plant modeling based on function-structure mutual feedback provides basis for the simulation of plant growth status in the next stage,and has important significance for the accurate simulation of the dynamic growth process of plant.

DSS Scheme for Operation Optimization Problem on Power Plants

Based on the target analysis of the operation optimization for power plants, a novel system scheme called operation optimization decision support system (OODSS) is brought forward. According to the structure and design thinking of decision support system (DSS), the overall structure of the OODSS is studied, and the scheme of the sub systems in the OODSS such as the user interface system, the problem processing system, the database system, the model base system, the expert system (ES) and the data mining sy...

Simulating leaf net CO_2 assimilation rate of C_3 & C_4 plants and its response to environmental factors

Basic structure and algorithm of leaf mechanism photosynthesis model were described in first part of this study based on former researcher results. Then, considering some environmental factors influencing on leaf photosynthesis, three numerical sensitivity experiments were carried out. We simulated the sing le leaf net CO2 assimilation, which acts as a function of different light, carbo n dioxide and temperature conditions. The relationships between leaf net photosy nthetic rate of C3 and C4 plant with CO2 concentration intercellular, leaf tempe rature, and photosynthetic active radiation (PAR) were presented, respectively. The results show the numerical experiment may indicate the main characteristic o f plant photosynthesis in C3 and C4 plant, and further can be used to integrate with the regional climate model and act as land surface process scheme, and bett er understand the interaction between vegetation and atmosphere.

MORPHOLOGICAL MODEL FOR RHEOLOGICAL PROPERTIES OF PLANT CELL SUSPENSION CULTURE SYSTEM

This paper puts forward a physical and mathematical model for the rheological properties of a plant cell suspension culture system.The model can explain why the system is pseudoplastic satisfactorily,thus the rheological properties of the system as the effect of the flow behavior index on plant cell concentration are interpreted correctly and the mechanism of the rheological properties of the system is further understood.Therefore the model can be applied in the technological design and optimum conditions of the system and the reformation,evaluation and scale up of reactors.