Plant Output Research Articles

Comparative transcriptomic and proteomic analyses of two salt-tolerant alfalfa (Medicago sativa L.) genotypes: investigation of the mechanisms underlying tolerance to salt

Abiotic stressors such as salt stress restrict plant development and output, which lowers agricultural profitability. In this study, alfalfa (Medicago sativa L.) varieties with different levels of salt tolerance were examined using high-throughput RNA sequencing (RNA-Seq) and Tandem Mass Tags (TMT) technologies to study the reactions of the root systems to salt stress, from transcriptomics and proteomics perspectives. The varieties Atlantic (AT) and Zhongmu-1 (ZM-1) were selected and evaluated after 2 h and 6 h of treatment with 150 mM NaCl. The results showed that under salt stress for 2 h, 1810 differentially expressed genes (DEGs) and 160 differentially expressed proteins (DEPs) in AT were screened, while 9341 DEGs and 193 DEPs were screened in ZM-1. Under salt stress for 6 h, 7536 DEGs and 118 DEPs were screened in AT, while 11,754 DEGs and 190 DEPs were screened in ZM-1. Functional annotation and pathway enrichment analyses indicated that the DEGS and DEPs were mainly involved in the glutathione metabolism, biosynthesis of secondary metabolites, glycolysis/gluconeogenesis, carbon fixation in photosynthetic organisms, and photosynthesis pathways. A series of genes related to salt tolerance were also identified, including GSTL3 and GSTU3 of the GST gene family, PER5 and PER10, of the PER gene family, and proteins such as APR and COMT, which are involved in biosynthesis of secondary metabolites. This study provides insights into salt resistance mechanisms in plants, and the related genes and metabolic pathways identified may be helpful for alfalfa breeding in the future.

Investigating the factors affecting sustainability of ready mix concrete plants: case study of Pune region

Construction industry plays a significant role in the economic development of the nation. Due to site and other constraints, concreting is preferred by ready mix concrete (RMC) plants. It is very well-known fact that the construction industry immensely contributed for CO2 emission mainly from cement-based materials. One of the way to achieve smart city objective to effectively manage ready mix concrete plants. While opting for RMC over site mixing offers various advantages. In this study, key factors governing the sustainability and energy consumption of RMC plants are identified. This study investigates the efficiency and sustainability of RMC production in Maharashtra, suggesting that its efficiency hinges on various factors. Through a constructivist perspective, the research examines twelve RMC plants in Pune mainly and other parts of Maharashtra, finding that power supply, plant output, and capacity require sustainability. While raw material supply is deemed sustainable, management methods and product control were found lacking. The study identifies challenges affecting demand, such as regulatory issues, management issues and technological limitations. The conclusion emphasizes the need for alternative and sustainable power sources, skilled labor, and improved business processes to enhance RMC production efficiency in Maharashtra, recommending measures like waste reuse and innovative advancements. This study will help the stakeholders in prioritizing the resources and process towards more sustainable way.

Fostering ayurvedic plant wellness: Innovative leaf disease detection using computer vision and machine learning

Ayurveda is a conventional medicinal approach that has its roots in India and has been used for hundreds of years. It is still popular today since it is entirely natural and free of side effects. Although ayurvedic medicines are made from natural botanical substances, their safety depends on the way they are administered, taking into account the needs of the individual and the specific disease states they are treating. Diseases that affect the plants' leaves are regarded to be one of the main reasons for a decline in the output of ayurvedic plants in India's agricultural, economic, cosmetic, and pharmaceutical sectors. A plant exhibits signs of plant diseases in diverse sections of the plant, however, leaves are the most frequently observed component for spotting an infection. The objectives of this work are to modernize and innovate an autonomous ayurvedic plant leaf disease detection system by examining the scientific basis of computer vision. Additionally, a full analysis of computer vision techniques such as image pre-processing, segmentation, and feature extraction is covered to detect defects in plant leaves. This work investigates cutting-edge machine learning methods for identifying plant diseases that employ a variety of computer vision techniques. Based on the review, the article addresses the challenges and offers recommendations for changes that could be made in the future.

Dynamic Event-Triggered Control for Sensor–Controller–Actuator Networked Control Systems

We consider the problem of output feedback stabilization of LTI systems under event-triggering implementation. In particular, we assume that both the plant output and the control input are both transmitted over the network in an asynchronous manner. To that end, two independent event-triggering rules are constructed to generate the transmission instants of the submitted signals. The proposed approach is dynamic in the sense that the triggering rules involve internal dynamical variables to allow for further reduction in the communication load. Moreover, the inter-transmission times for both sides of the channel are lower bound by enforced dwell times to prevent the occurrence of Zeno phenomena. The problem is challenging due to mutual interactions between the sampling errors of the plant output and the control input, which requires careful handling to ensure closed-loop stability. The triggering mechanisms are designed by emulation as we first ignore the effect of the network and stabilize the plant in continuous-time. Then, the communication constraints are taken into account to derive the triggering conditions such that the stability of the networked control system is preserved. The required conditions are formulated in terms of a linear matrix inequality. The effectiveness of the technique is demonstrated by numerical simulations.

The effects of geometric factors on power generation performance in solar chimney power plants

This research aims to create an admissible source for specifying the relation between the plant's output power and specified geometrical parameters unique to each simulated chimney dimension. In the current study, the solar chimney output power has been studied considering 49 solar chimneys, including different collector diameters, chimney heights, and chimney diameters. The study employs the Ansys-CFX to simulate the numerical model for the unsteady k-ε turbulent flow for a vast number of geometries. The obtained results indicate that the velocity as the main factor in generating the output power has significantly improved by elevating the collector radius. The output power increased up to 50 % by elevating the collector radius and around 30 % by enhancing the chimney heights which cause an increase in airflow, temperature differentials. Moreover, increasing the chimney height has generally ascended the power plant system. Among all geometrical parameters, chimney radius had the least impact on solar chimney performance. The results indicate that the best ratio between collector radius and chimney height is 6 %–10 % to achieve the optimized output power. Our findings were verified with the available experimental setups.

Study of Plant Growth Promoting Abilities of Rhizobium Strains Isolated from Mungbean Root Nodules

Background: Plant Growth Promoting Rhizobacteria (PGPR) are a type of bacteria that use biofertilizers and other plant growth-promoting agents to increase plant output and growth. In the last several decades, there has been a global surge in the usage of beneficial soil microorganisms like PGPR for safe and sustainable agriculture due to the detrimental effects of artificial fertilizers on the environment and their rising prices. Plant-beneficial rhizobacteria have the potential to reduce the world's reliance on dangerous agricultural chemicals that upset agro-ecosystems. Today a large part of yield is lost due to the various stress mechanisms of plants. It could be classified into biotic and abiotic stress. These are generally a group of microorganisms that is found either in the rhizosphere or in the root nodules of plants. PGPR has beneficial impact, they colonize in the plant roots and improve crop yields and agricultural productivity and also produce some useful growth promoting growth hormones viz., IAA, Auxins, Cytokinin and Gibberellins. Methods: The bacterial isolates were screened for plant growth promoting traits such as phosphate solubilization, IAA production and siderophore production was tested qualitatively by using pikovskayas media, TSB (Tryptone Soy Broth) agar and Chrome Azurol Sulphate (CAS) blue agar respectively. Results: In present study out of the eight rhizobial isolates, five exhibited the phosphate solubilization zones, three shows negative result. However out of eight isolates five isolates produced IAA and three showed negative result. Among all isolates seven isolates were able to produce siderophore, while one showed negative result.

Plant Robots: Harnessing Growth Actuation of Plants for Locomotion and Object Manipulation.

Plants display physical displacements during their growth due to photosynthesis, which converts light into chemical energy. This can be interpreted as plants acting as actuators with a built-in power source. This paper presents a method to create plant robots that move and perform tasks by harnessing the actuation output of plants: displacement and force generated from the growing process. As the target plant, radish sprouts are employed, and their displacement and force are characterized, followed by the calculation of power and energy densities. Based on the characterization, two different plant robots are designed and fabricated: a rotational robot and a gripper. The former demonstrates ground locomotion, achieving a travel distance of 14.6mm with an average speed of 0.8mmh-1. The latter demonstrates the picking and placing of an object with a 0.1-g mass by the light-controlled open-close motion of plant fingers. A good agreement between the experimental and model values is observed in the specific data of the mobile robot, suggesting that obtaining the actuation characteristics of plants can enable the design and prediction of behavior in plant robots. These results pave the way for the realization of novel types of environmentally friendly and sustainable robots.

Controller switching with the anti-bump adapter

Switching on a new or retuned controller while keeping the control loop engaged tends to generate transient “bumps” in the commands and plant outputs. For a linear time-invariant (LTI) controller in state–space form, these bumps can be mitigated or even suppressed by resetting the controller state to an appropriate non-zero value adapted to the pre-switching trajectories of the feedback loop signals. This paper proposes a simple way to compute this adapted controller state by minimizing the difference between the commands actually applied immediately before switching and the virtual command trajectory which the new controller would have generated if it had been activated in parallel. This adapted state can be recursively computed as the output of an LTI system in standard state–space form, the anti-bump adapter. Computing the anti-bump adapter involves only standard matrix algebra and control concepts (e.g., observability and controllability matrices/Gramians). Calculations are very simple and can be translated into compact computer code. This bump suppression procedure is applicable to any discrete-time or continuous-time LTI SISO or MIMO controller, without any assumption on or knowledge of either the previously active controller or the plant. Illustrative applications to continuous-time and discrete-time switching problems and a Matlab® code for computing the anti-bump adapter are presented.

Firms and Labor in Times of Violence: Evidence from the Mexican Drug War

ABSTRACT This paper examines the impact of violence resulting from drug trafficking on manufacturing firms in an emerging economy. By utilizing comprehensive longitudinal data spanning all of Mexico from 2005 to 2010, and employing an instrumental variable strategy that leverages plausibly exogenous spatiotemporal variations in the homicide rate during the outbreak of drug violence, the analysis reveals a significant negative effect of violence on plant output, employment, product scope, and capacity utilization. The negative effect on employment is entirely driven by blue-collar employment and concentrated among low-wage, female-intensive firms. Further, consistent with a violent-environment-induced blue-collar labor-supply shock, the results show positive effects on blue-collar wages and negative effects on white-collar wages at the firm level. Output resilience to violence is also shown to be lower among labor-intensive, domestically selling and sourcing, less diversified firms. These findings show the rise of drug violence has a significant negative effect on development of domestic industrial capability in Mexico and shed light on the characteristics of the most affected firms and the channels through which they are affected.

Long-term power forecasting of photovoltaic plants using artificial neural networks

Increasing the robustness of forecasting energy production models from a photovoltaic plant is crucial in investment decisions, while profitability is closely linked to its location since its energy production depends on the local climate conditions. This study aimed to mitigate uncertainty regarding the installation of photovoltaic systems by addressing the challenge of forecasting electrical energy production over the long term. Artificial Neural Network models were used for this purpose, predicting the power output of a photovoltaic plant based on the ambient temperature, cell temperature, and solar irradiance. Data recorded every minute over one year at an experimental photovoltaic plant revealed a strong correlation between energy production and the input variables. This research compared the performance of multilayer perceptron, feedforward, long short-term memory, and modular artificial neural networks architectures. Validation was carried out using mean squared error, mean absolute error, mean absolute percentage error, root mean squared error, and coefficient of determination as key metrics. Notably, the long short-term memory network demonstrated the highest accuracy in energy forecasting achieving a mean squared error of 0.0089 p.u, a mean absolute error of 0.0527 p.u, a root mean squared error of 0.0944 p.u, and a mean absolute percentage error of 49.0124 %. Nonetheless, the modular model came close to long short-term memory accuracy but with lower computational cost.

Modeling the output from a commercial chemical process using regression models from survival analysis

This article is concerned with the modeling of the gas output of a commercial chemical plant using the coal sources as predictor variables. We consider the use of two models to incorporate these predictors; the Cox proportional hazards and accelerated failure time regression models. These models are chosen for their simplicity and for the ease with which the effects of explanatory variables can be interpreted. The contribution of this article lies therein that these models are used in the current context for the first time. We show, using both graphical and formal hypothesis testing procedures that these models (with a Weibull baseline distribution) fit observed gas production data well. We provide a discussion of the interpretation of the estimated model parameters and we comment on how these estimates can be of substantial practical value. The large scale of production from the chemical plant in question ensures that potential cost savings and increases in production associated with more accurate models are of great practical importance.

Megacity pathways in China under the dual carbon goal: The case of Shanghai

The pathways to achieving carbon neutrality at the city level are diverse due to varying energy supply and demand conditions. Shanghai faces obstacles such as limited land resources, high costs of renewable energy technologies, and instability of renewable energy. These challenges hinder the city’s efforts to achieve carbon peak and carbon neutrality (dual carbon). Therefore, Shanghai must identify and optimize its development path for renewable energy under the dual carbon goal. We employed the Low Emissions Analysis Platform-Shanghai (LEAP-SH) model to simulate the impact of policies, such as industrial upgrading, energy efficiency improvement, energy structure optimization, increased technical innovation on energy, and ecological restoration, on the carbon emission pathways from 2022 to 2060 using five different scenarios. Our results indicate that Shanghai has the potential to achieve carbon neutrality in 2059 by promoting carbon reduction, pollution control, and green expansion. Moreover, we determined that the manufacturing industry; power generation industry; and transportation, storage, and mail services are the three major sectors for emission reduction under the dual carbon goal. Furthermore, the capacity and output of coal-fired power plants will be gradually replaced by offshore wind power in the dual carbon pathway. Finally, this study proposes countermeasures and suggestions for Shanghai to attain the dual carbon goal and high-quality development.

Bribery, plant size and size dependent distortions

I document that small plants spend a higher fraction of their output on bribery than big plants, and that non-bribe-paying plants face higher distortions compared to bribe-paying plants in Türkiye. I develop a one-sector growth model in which size-dependent distortions, bribery opportunities, and different plant sizes coexist. In the model, plants are able to avoid distortions through bribery. The model parameters are calibrated with distortions and bribery opportunities in order to account for the plant size distribution as well as bribery expenditures by different plant sizes in the Turkish data. Counterfactual exercises show that size-dependent distortions become less distortionary in the presence of bribery opportunities. An increase in the size dependency of distortions has smaller aggregate effects since plants are able to circumvent distortions by paying larger bribes. Quantitatively, when bribery opportunities are present in the economy, mean plant size and output are 7.8 and 2.0 percent higher, respectively.

The Value of Drilling—A Chance-Constrained Optimization Approach

Managing uncertainty is a core challenge in mine planning. Mine planners often represent various planning variables, such as equipment performance and geological parameters, as random variables due to inherent uncertainties. This paper looks at geological uncertainty and its impact on mine planning. Some traditional approaches to manage this uncertainty include using conditional simulations or mathematical programming in the planning process. Drilling additional holes, despite its cost, is a common method to reduce uncertainty using additional samples to reduce deposit variance. In this paper, we first outline an ore blending optimization model which uses chance-constrained programming to manage property limit risk when selecting the order of ore feed into a processing facility. In coal mining, in tactical planning horizons, the order of coal seam removal is usually predetermined, allowing a blending model to ensure optimal feed properties. Using chance-constrained programming allows us to blend the uncertainties from geological models to maximize plant output while adhering to property constraints. We use the chance-constrained blending model to determine the value of additional information from infill drilling. The model prioritizes drilling locations that reduce uncertainty and improve blending outcomes. A case study on a coking coal mine in Queensland, Australia, demonstrates the model’s application, highlighting significant improvements in blending by reducing the variance of high-quality blocks. The study concludes that targeting high-quality blocks for variance reduction can better accommodate lower-quality material, offering a more valuable approach than the traditional focus of reducing uncertainty in low-quality blocks. This approach provides insights for improving mine planning strategies and showcases the potential of chance constraints in optimizing ore blending under uncertainty.

Predicting power and solar energy using neural networks and PCA with meteorological parameters from Diass and Taïba Ndiaye

The excessive reliance on conventional fossil fuel-based resources poses a significant threat to our environment. To mitigate this impact, it has become increasingly crucial to increase the integration of intermittent and non-polluting energy sources into our electrical grids. However, while this higher penetration rate brings benefits such as improved producer satisfaction and reduced fossil fuel consumption, it also presents challenges for traditional non-smart electrical networks. To promote intermittent energy sources effectively and maintain a balance between consumption and production, accurate forecasting of these energy outputs plays a vital role. This research paper focuses on studying the application of artificial neural networks for predicting the power and energy output of the Diass solar power plant in the short and medium term. The proposed approach utilizes not only the meteorological data from the city where the power plant is located but also data from a nearby city with a data acquisition station. Principal component analysis (PCA) is employed to select the relevant variables for the prediction model. Furthermore, the results obtained from our approach are compared to existing literature that solely uses meteorological data from the power plant's location. The comparison shows that our method achieves more satisfactory results, with mean absolute errors and root mean square errors of 0.0223 KWh and 0.003 KWh, respectively, and a prediction accuracy of 94.57% in terms of energy and power. It is worth noting that the computational resource requirements for our approach are higher, with simulation times ranging between 1788 seconds and 2201 seconds. By utilizing a broader range of data sources and employing advanced techniques like artificial neural networks, this research contributes to improving the accuracy of solar power generation forecasts. The findings highlight the potential of incorporating additional data inputs and advanced modeling techniques to enhance the performance of renewable energy systems, paving the way for a more sustainable and efficient energy future.

Sustainable potato farming in Shandong Province, China: a comprehensive analysis of organic fertilizer applications

IntroductionThe potato holds the distinction of being the world’s largest non-cereal food crop and ensuring its sustainable production is imperative for global food security. Notably, China leads in both the planting area and output of potatoes globally, cementing its crucial role in the nation’s agricultural economy. A scientific assessment of the effectiveness of organic fertilizers on potato cultivation can significantly contribute to the promotion of sustainable agriculture.MethodsThis study utilizes a Propensity Score Matching (PSM) model and introduces a novel cost-efficiency approach to analyze and evaluate the production efficiency and economic impact of organic fertilizer application among 546 potato growers in Shandong.ResultsThe research findings reveal the following: Firstly, compared to the control group without organic fertilizer application, it is evident that the use of organic fertilizers enhances production technology efficiency, labor productivity, land productivity, and net profit per unit by 3.6%, 1588.47 kg/person, 16346.77 kg/ha, and 16135.32 yuan/ha, respectively. Secondly, an examination of cost efficiency among growers with different production scales indicates that those with a planting scale of 0.667-1.333 hectares demonstrate relatively high production efficiency across multiple factors. Additionally, there is an observable inverted U-shaped trend in the relationship between planting scale and production efficiency. Thirdly, the continuous application of organic fertilizers proves advantageous in mitigating inefficiencies in investment techniques, leading to cost savings and efficiency improvements in potato cultivation.DiscussionConsequently, it is recommended that the government and relevant departments enhance technical support, elevate professional training programs, and optimize the allocation of input factors. These measures aim to encourage farmers to adopt organic fertilizers, thereby promoting sustainable agricultural practices.

Optimizing solar power efficiency in smart grids using hybrid machine learning models for accurate energy generation prediction

The fourth energy revolution is characterized by the incorporation of renewable energy supplies into intelligent networks. As the world is shifting towards cleaner energy sources, there is a need for efficient and reliable methods to predict the output of renewable energy plants. Hybrid machine learning modified models are emerging as a promising solution for energy generation prediction. Renewable energy generation plants, such as solar, biogas, hydropower plants, wind farms, etc. are becoming increasingly popular due to their environmental benefits. However, their output can be highly variable and dependent on weather conditions, making integrating them into the existing energy grid challenging. Smart grids with artificial intelligent systems have the potential to solve this challenge by using real-time data to optimize energy production and distribution. Although by incorporating sensors, analytics, and automation, these grids can manage energy demand and supply more efficiently, reducing carbon emissions, increase energy security, and improve access to electricity in remote areas. However, this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN). For this purpose, this study considers various parameters of a solar plant such as power production (MWh), irradiance or plane of array (POA), and performance ratio (PR). The HCLN model demonstrates superior accuracy with the RMSE values of 0.012027 for MWh, 0.013734 for POA and 0.003055 for PR, along with the lowest MAE values of 0.069523 for MWh, 0.082813 for POA, and 0.042815 for PR. The obtained results suggest that the proposed machine learning models can effectively enhance the efficiency of solar power generation systems by accurately predicting the required measurements.

Optimization of steam parameters in double-pressure heat recovery steam generators

Double-pressure heat recovery steam generators are installed in medium-size combined cycle power plants. For a fixed total heating surface area of heat recovery steam generator, the net power output of the power plant depends on steam pressures and temperatures. This paper presents a method of determining the optimal set of steam parameters in double-pressure heat recovery steam generators without reheating and with reheating. The mathematical models of both heat recovery steam generators are presented, and the optimization procedure is described. For given exhaust gas temperature and total heating surface area, the optimal steam parameters of both heat recovery steam generators that maximize net power output can be found subjected to the constraint that the steam quality at turbine outlet is not less than 88 %. Simulation results show that reheating increases optimal net power output by 2.4 % and exhaust gas temperature by about 80 °C.

Review and Modeling of Integrated Energy Systems with Nuclear Reactor Coupled Desalination and District Heating

Detailed reviews of a past advanced nuclear reactor–based integrated energy system, as well as other nuclear reactor and fossil fuel–based integrated energy systems, have been performed for this work. A review of the utilization of heat from nuclear reactors for various applications and cogeneration has been done. The heat can be utilized by extracting the steam from the turbine while the steam is still at a desired temperature. While the use of nuclear process heat for district heating in countries like Finland, France, China, Poland, and elsewhere is discussed, more focus of the review has been given to nuclear desalination processes. Integrated energy systems (IESs), where distinct types of reactors like pressurized water reactors, boiling water reactors, sodium-cooled fast reactors, heavy water reactors, and other advanced reactors are coupled with various nuclear desalination processes, like multi-effect distillation (MED), multistage flashing, and reverse osmosis methods, are discussed. The nuclear desalination plant at Aktau is discussed in more detail due to its decades of successful operation. The IES of the Aktau plant coupled with a five-effect MED desalination plant was taken as a reference for modeling the Open Modelica (OM)–based IES in this work. The OM IES model shows good agreement with the MED plant output of Aktau and can be extended for future applications of IESs.

Grid impact analysis on wind power plant interconnection in strengthening electricity systems

The Timor system is one of the large systems in the East Nusa Tenggara region. Based on the general plan for electricity supply for 2021-2030, there is a plan to interconnect a 2x11 MW wind power plant. The addition of wind power plants will pose a considerable threat to the system due to the intermittency nature of renewable energy plants. Therefore, a comprehensive grid impact study is needed to convince network managers that adding wind farms will not cause disruptions to the system either locally or in general and is expected to strengthen the electricity system. The power flow simulation results, installing a 2x11 MW wind farm on the Timor system can improve voltage quality and reduce losses on both 70 and 150 kV systems. For transient stability, the frequency value on the Timor system still meets the grid code requirements. In addition, the simulation results of the intermittency impact of the wind power plant output show that the Timor system is still in a stable condition. The stability of the rotor angle of the existing power plant when the transient stability simulation is carried out shows that it is still in a balanced condition.