Plant Strategies Research Articles

Characterising the Genomic Landscape of Differentiation Between Annual and Perennial Rye.

Annuality and perenniality represent two different life-history strategies in plants, and an analysis of genomic differentiation between closely related species of different life histories bears the potential to identify the underlying targets of selection. Additionally, understanding the interactions between patterns of recombination and signatures of natural selection is a central aim in evolutionary biology, because patterns of recombination shape the evolution of genomes by affecting the efficacy of selection. Here, our aim was to characterise the landscape of genomic differentiation between weedy annual rye (Secale cereale L.) and wild perennial rye (Secale strictum C. Presl), and explore the extent to which signatures of selection are influenced by recombination rate variation. We used population-level sequence data of annual and perennial rye to analyse population structure and their demographic history. Based on our analyses, annual and perennial rye diverged approximately 26,500 years ago (ya) from an ancestral population size of ~85,000 individuals. We analysed patterns of genetic diversity and genetic differentiation, and found highly differentiated regions located in low-recombination regions, indicative of linked selection. Although all highly differentiated regions, as revealed by F ST-outlier scans, were located in low-recombining regions, not all chromosomes showed this tendency. We therefore performed a gene ontology enrichment analysis, which showed that highly differentiated regions comprise genes involved in photosynthesis. This enrichment was confirmed when F ST outlier scans were performed separately in low- and intermediate-recombining regions, but not in high-recombining regions, suggesting that local recombination rate variation in rye affects outlier scans. Cultivated rye is an annual crop, but the introduction of perenniality may be advantageous in regions with poor soil quality or under low-input farming. Although the resolution of our analysis is limited to a broad-scale, knowledge about the evolutionary divergence between annual and perennial rye might support breeding efforts towards perennial rye cultivation.

Research on the collaborative operation strategy of shared energy storage and virtual power plant based on double layer optimization

Large-scale access to distributed energy resources leads to new energy consumption problems and safe operation risks in the power system. Virtual power plants and shared energy storage are effective ways to promote the flexible consumption of distributed energy resources and improve the reliability and economy of power system operation. Based on the concept of sharing economy and considering the complementary characteristics of source and load resources between different virtual power plants, this paper focuses on the optimisation of shared energy storage and multi-virtual power plant operation. Firstly, distributed wind power, distributed photovoltaic and flexible load resources are aggregated into virtual power plants to analyze the cooperative operation mode of shared energy storage and multi-virtual power plant systems. Secondly, a two-layer decision model for shared energy storage configuration and multi-VPP system operation optimisation is constructed, with the upper model solving the optimal energy storage configuration scheme by maximising the revenue of the shared energy storage operator, and the lower model optimising the multi-VPP system operation strategy by minimising the total operation cost, the maximum amount of new energy consumed, and the minimum amount of carbon emission as the multi-objective optimisation strategy. Finally, a simulation analysis is carried out, and the results show that compared with the independent operation mode of each virtual power plant, the model proposed in this paper increases the annual profit of the shared energy storage operator by 7180¥, reduces the operating cost of the VPP system by 7.08 %, improves the rate of renewable energy consumption by 0.82 %, and reduces the annual carbon emission by 54.91 t, which realizes the mutual benefits of the virtual power plant and the shared energy storage.

Signal-devices management and data-driven evidential constraints based robust dispatch strategy of virtual power plant

Ensuring the safety and reliability of energy systems is very important in power grid operation. However, inherent intricacies and uncertainties of modern-day power systems, such as the mismatch between signal frequency and equipment response speed and the stochasticity associated with renewable energy and load forecasts, create significant challenges for generation dispatch planning. This study proposes a robust optimization approach with constraints based on signal-devices management and data-driven evidential distribution constraints. The first stage of this approach is to decompose and recombine the net power demand according to the Hilbert-Huang transform and device capability. The signal-device management constraints are then established based on this. The second stage formulates an evidential constraint based on data-driven evidential distribution and chance constraints in a distributionally robust optimization framework that caters to the stochasticity associated with renewable energy and load forecasts. The proposed model is validated on a virtual power plant to assess the approach’s efficacy for different dispatching scenarios. Penalty-based sensitivity analysis provides further insights into the proposed method’s performance for varying levels of CO2 emissions. Simulation results demonstrate that system flexibility becomes increasingly crucial for maintaining system stability and security as the penetration of renewable energy grows. Compared with chance constraint, the proposed data-driven evidential constraint effectively enables the optimization framework to handle stochasticity after sacrificing 0.62% more economic loss and 0.7% more environmental loss. Excessively high penalty parameters for CO2 do not promote economic development, resulting in 21.08% and 52.51% more economic and environmental losses without obvious environmental protection.

Optimal operation strategy for renewable power plants based on 5G base stations response

Abstract The integration of large-scale new energy sources has led to a significant challenge in electricity supply and demand balancing within the power system. To address this issue, it is crucial to establish an optimization strategy of renewable power plant based on 5G base stations response. Firstly, according to the operating state of the base station, the energy storage schedulable strategy of the base station was designed. Then, through the regulation function of the base station’s reserve energy storage and the operation principle of the renewable energy power plant, the operation model of the renewable power plant considering the base station’s participation and response was established. Finally, evaluation indexes such as the absolute peak-to-valley difference, the standard deviation of daily load and the peak-valley difference rate can be used to measure system optimization results. The results show that the proposed strategy improves the source load imbalance in the power system while enhancing reliability and economy of new energy.

Ralstonia solanacearum Infection Drives the Assembly and Functional Adaptation of Potato Rhizosphere Microbial Communities.

Bacterial wilt caused by Ralstonia solanacearum is a destructive disease that affects potato production, leading to severe yield losses. Currently, little is known about the changes in the assembly and functional adaptation of potato rhizosphere microbial communities during different stages of R. solanacearum infection. In this study, using amplicon and metagenomic sequencing approaches, we analyzed the changes in the composition and functions of bacterial and fungal communities in the potato rhizosphere across four stages of R. solanacearum infection. The results showed that R. solanacearum infection led to significant changes in the composition and functions of bacterial and fungal communities in the potato rhizosphere, with various microbial properties (including α,β-diversity, species composition, and community ecological functions) all being driven by R. solanacearum infection. The relative abundance of some beneficial microorganisms in the potato rhizosphere, including Firmicutes, Bacillus, Pseudomonas, and Mortierella, decreased as the duration of infection increased. Moreover, the related microbial communities played a significant role in basic metabolism and signal transduction; however, the functions involved in soil C, N, and P transformation weakened. This study provides new insights into the dynamic changes in the composition and functions of potato rhizosphere microbial communities at different stages of R. solanacearum infection to adapt to the growth promotion or disease suppression strategies of host plants, which may provide guidance for formulating future strategies to regulate microbial communities for the integrated control of soil-borne plant diseases.

Optimal strategies for virtual power plants to participate in medium- and long-term power trading in the context of contracts for difference (CFDs)

Abstract In the context of virtual power plants gradually joining the medium-and long-term power market transactions, the decision-making problem of this new subject in the transaction process has attracted wide attention. Since virtual power plants possess distributed power sources on the supply side, as well as variable loads, energy storage, and other resources on the customer side, they are able to participate in both the sale and purchase of electricity in the medium-and long-term electricity market at the same time, which leads to the need for the relevant analysis to be divided into the two cases of sale and purchase of electricity. At the same time, due to the wide application of the CFD mechanism, power transactions should also take into account the compensation adjustment brought by this mechanism in addition to the market price. To sum up, this paper constructs a static game model for the virtual power plant’s power sales and purchasing strategies in the medium-and long-term power market under the premise of the CFD mechanism. The analysis shows that the optimal trading volume is related to the CFD volume, the total volume of power supplied by the bidding, the market price, etc. The relevant conclusions will help market managers to grasp the market price. The conclusions of the analysis help market managers grasp the key transaction information and assist virtual power plants to participate in medium-and long-term power transactions in an orderly manner.

Cultivating resilience: use of water deficit to prime peanut production and improve water stress tolerance

Regulated deficit irrigation is a potential strategy for priming peanut plants to improve their acclimation to water stress. To assess possible effects of priming and develop an effective water stress priming strategy, greenhouse experiments were conducted to compare primed and non-primed peanut cultivars, C7616 and TUFRunner ‘511’TM, to subsequent water stress. Plants were divided into 1) controls with daily irrigation to field capacity throughout the entire growth cycle, 2) non-primed plants receiving daily irrigation up to 55 to 65 days after planting (DAP), followed by exposure to mid-season water stress, and 3) primed plants that received 50% of the control irrigation either from 5-45 DAP (long-term priming), or 20-45 DAP (short-term priming), followed by mid-season water stress at 55 to 65 DAP. An automated physiological phenotyping platform was used to control irrigation and continuously monitor, soil water content, whole-plant transpiration and water use. Single-leaf measurements of net CO2 assimilation, stomatal conductance, and transpiration were taken periodically. Plant biomass and biomass partitioning were also determined. Results indicated that primed plants grown under water deficit exhibited either reduced (acclimated) or intensified (sensitized) physiological stress responses upon recurrent water stress. Timing and duration of the priming period played a key role in modulating plant phenotypic plasticity, which varied by genotype, suggesting that priming could be in part genetically controlled.

Change in root morphology, growth, and P transfer: Does foliar P application restrain root P-foraging behavior of Cunninghamia lanceolata seedlings in P-deficient environments?

Although foliar phosphorus application (FPA) is a fertilization method that can rapidly supplement nutrient elements in plants, it remains unclear whether it significantly affects the response strategy of roots under environmental stress. In this study, changes in plant growth, biomass allocation, and P use efficiency (PUE) of Chinese fir in different P environments were analyzed by FPA and root P application (RPA) treatments. The effects of FPA on P-foraging behavior of Chinese fir root system in P-deficient environment were then discussed. The root biomass with RPA was more significant at 0.03-0.25mmol·L-1 P concentrations than that with FPA; however, the PUE was significantly reduced by 28.89–29.41%. With an increase in the P application concentration, under the effect of FPA, the Chinese fir roots mainly reduced root proliferation by decreasing the tip number, surface area and volume but increasing in diameter, as well as the PUE of root, aboveground. As a result, the entire plant was improved. However, the degree of morphological adjustment of the root system was lower than that under RPA. In summary, both FPA and RPA can alleviate P starvation in Chinese fir to a certain extent, thereby improving PUE. Compared with RPA, FPA had a more significant impact on the behavior of Chinese fir roots in sensing and responding to soil P content, and the roots could more quickly sense changes in P content in the body. This mechanism provides valuable insights into the root response strategies of plants in P-deficient environments.

Independent and interactive effects of N and P additions on foliar P fractions in evergreen forests of southern China

Fertilization or atmospheric deposition of nitrogen (N) and phosphorus (P) to terrestrial ecosystems can alter soil N (P) availability and the nature of nutrient limitation for plant growth. Changing the allocation of leaf P fractions is potentially an adaptive strategy for plants to cope with soil N (P) availability and nutrient-limiting conditions. However, the impact of the interactions between imbalanced anthropogenic N and P inputs on the concentrations and allocation proportions of leaf P fractions in forest woody plants remains elusive. We conducted a meta-analysis of data about the concentrations and allocation proportions of leaf P fractions, specifically associated with individual and combined additions of N and P in evergreen forests, the dominant vegetation type in southern China where the primary productivity is usually considered limited by P. This assessment allowed us to quantitatively evaluate the effects of N and P additions alone and interactively on leaf P allocation and use strategies. Nitrogen addition (exacerbating P limitation) reduced the concentrations of leaf total P and of different leaf P fractions. Nitrogen addition reduced the allocation to leaf metabolic P but increased the allocation to other fractions, while P addition showed opposite trends. The simultaneous additions of N and P showed an antagonistic (mutual suppression) effect on the concentrations of leaf P fractions, but an additive (summary) effect on the allocation proportions of leaf P fractions. These results highlight the importance of strategies of leaf P fraction allocation in forest plants under changes in environmental nutrient availability. Importantly, our study identified critical interactions associated with combined N and P inputs that affect leaf P fractions, thus aiding in predicting plant acclimation strategies in the context of intensifying and imbalanced anthropogenic nutrient inputs.

Influence of endophytic fungi treatments on aluminum contents in Vernicia montana seedlings and soils under different concentrations of aluminum stress

Although leveraging the interaction with endophytic fungi is an efficient and environment-friendly strategy for plants to enhance growth and resistance, how different endophyte species influence host plants’ resilience in adverse conditions remain comparatively unclear. In order to explore the effect of endophytic fungi on the aluminum resistance of woody host plants, Vernicia montana seedlings were subjected to different aluminum concentrations (T0, T1, T2, T3, T4) in this study. The aluminum contents in roots, leaves and rhizospheric soil of V. montana seedlings were determined after applying endophyte suspensions of Pestalotiopsis (NP), Alternaria (LA), Penicillium (QP), Coniothyrium (DC) and Thermophilic (ST) spp. The results showed that aluminum stress treatment, endophytic fungi treatment and their interaction had significant effects on aluminum content in leaves, aluminum content in roots, aluminum content in rhizospheric soil, and the transport and retention rate of aluminum ions in soil-root-leaf. With the increase of aluminum concentrations, the aluminum content in leaves of V. montana increased in the endophyte treatments of LA and ST, decreased in CK, NP and DC, or had marginal variation in QP treatment. Compared with T0, four endophyte treatments of LA, QP, DC and ST significantly reduced root aluminum content under T4 concentration (P < 0.05), contrary to the results of NP treatment. Endophyte treatments significantly increased root aluminum content of V. montana under T1 concentration (P < 0.05). The foliar Al content in fungi-inoculated seedlings was significantly lower than that of the non-inoculated ones under T0 and T3 levels (P < 0.05), the LRR is less than 1, while the opposite trend was observed under T2 and T4 treatments. The aluminum transport coefficient TFsoil-root and TFroot-leaf increased in different proportions under the same aluminum concentration. The findings indicate that the application of endophytic fungi change the aluminum contents and transport from rhizospheric soil, roots to leaves. The specific effects of endophytic fungi vary with the degree of aluminum stress and the fungi genus. The study proves that inoculation of endophytic fungi can improve the aluminum tolerance of host plants, and thereby play an important role in promoting the sustainable development of forestry.

Understory plant biodiversity is inversely related to carbon storage in a high carbon ecosystem.

Given that terrestrial ecosystems globally are facing the loss of biodiversity from land use conversion, invasive species, and climate change, effective management requires a better understanding of the drivers and correlates of biodiversity. Increasingly, biodiversity is co-managed with aboveground carbon storage because high biodiversity in animal species is observed to correlate with high aboveground carbon storage. Most previous investigations into the relationship of biodiversity and carbon co-management do not focus on the biodiversity of the species rich plant kingdom, which may have tradeoffs with carbon storage. To examine the relationships of plant species richness with aboveground tree biomass carbon storage, we used a series of generalized linear models with understory plant species richness and diversity data from the USDA Forest Service Forest Inventory and Analysis dataset and high-resolution modeled carbon maps for the Tongass National Forest. Functional trait data from the TRY database was used to understand the potential mechanisms that drive the response of understory plants. Understory species richness and community weighted mean leaf dry matter content decreased along an increasing gradient of tree biomass carbon storage, but understory diversity, community weighted mean specific leaf area, and plant height at maturity did not. Leaf dry matter content had little variance at the community level. The decline of understory plant species richness but not diversity to increases in aboveground biomass carbon storage suggests that rare species are excluded in aboveground biomass carbon dense areas. These decreases in understory species richness reflect a tradeoff between the understory plant community and aboveground carbon storage. The mechanisms that are associated with observed plant communities along a gradient of biomass carbon storage in this forest suggest that slower-growing plant strategies are less effective in the presence of high biomass carbon dense trees in the overstory.

Effects of Plant Extracts on Growth Promotion, Antioxidant Enzymes, and Secondary Metabolites in Rice (Oryza sativa) Plants

Plant extracts are widely used in sustainable agriculture practices to enhance crop production and reduce chemical usage in agriculture. This study employed several extraction solutions of various plant extracts to synthesize planting and spraying strategies, assess the persistence efficacy of rice, and investigate the influence of selected water extracts on secondary chemicals at different rice planting stages. Among 17 water extracts that were evaluated on rice seeds, 7 were enhanced to align with the lengths of rice roots 50–70% and shoots 40–50%. The analysis of extraction, spraying, and planting experiments revealed that water extracts, soil application, and transplanting were the most efficient methods for stimulating rice growth, especially 0.1 and 0.5% concentrations. The efficacy of the extracts remained intact also after 14 days of treatment. This study showed that photosynthesis and antioxidant activities may play crucial roles in plant growth. Rice growth stimulation has been linked to photosynthesis, flavonoid contents, and antioxidant enzymes, providing a balanced supply of nutrients for plant growth. Among all tested water extracts, Psidium guajava, Aloe vera, Allium sativum, and Medicago sativa extracts can be used to promote plant growth in organic farming.

Odor emission pattern of the waste storage workshop of kitchen waste treatment plant and control strategy study with CFD simulation.

Odor emission has become a great issue for kitchen waste management plants. Among all, unorganized emission source such as waste storage tank is the key cause. It is necessary to understand the odor emission characteristics and provide a proper control solution. In this study, a typical kitchen waste treatment plant located in Guangdong Province of China was selected to investigate the odor emission characteristics. According to the survey, the main complaint due to odor emission is on waste storage workshop. Hence, its odor emission has been investigated in this study. The gas samples were collected from the workshop in different season. According to the results, the odor emission during summer is the worst. In total, 105 odorous gases were detected from the waste storage workshop. The main odorous gases can be categorized into sulfur compounds, oxygen-containing organic compounds and terpenes. In specific, ethanol, acetic acid, methylmercaptan, α-pinene, methioether and limonene were the major odorous pollutants. Based on grey correlation, principal component analysis (PCA) and step-up regression analysis, methylmercaptan contributes the most to the odor concentration. It suggests that the odor emission control should pay more attention on methylmercaptan. The Computational Fluid Dynamics (CFD) stimulation was employed to investigate the odor distribution with applying air blowing as a curtain to separate the inside and outside atmosphere or suction to vacuum the inside air to prevent the odor emission. It was found that it could efficiently prevent odor emission by setting a 45° inclined air suction port at the top of the entrance gate. The study provides a theoretical basis on odor control for the waste storage workshop of kitchen waste management plants.

Frequency response coefficient optimal allocation strategy for multi-stacks alkaline water electrolyzer plant

Currently, power discrepancies between stacks are often overlooked when developing frequency response (FR) strategies for multi-stack alkaline water electrolyzer plants (AWEPs), which limits the full utilization of the plant’s FR potential. To address this issue, this paper introduces a stack-level FR coefficient allocation strategy aimed at minimizing post-contingency frequency deviation. Specifically, the quantitative relationship between the operating power of alkaline water electrolyzer (AWE) stacks and steady-state frequency deviation is first derived, followed by the formulation of an optimal FR problem. The solution to this problem is an FR strategy based on the principle of equal frequency deviation, which optimally allocates FR coefficients among AWE stacks according to their operating power, thereby maximizing the plant’s FR support capacity. Equivalent circuit model-based case study demonstrates that, compared to existing strategies, the proposed approach achieves a more balanced power distribution among the stacks during the FR process. This even power allocation not only reduces post-contingency frequency deviation but also enhances productivity and streamlines production management in the AWEP, thereby increasing the plant owner’s willingness to participate in FR. Specifically, the proposed strategy results in a 29.3% reduction in post-contingency frequency deviation, a 1.8% improvement in production efficiency under a 10 MW power imbalance, a 2.3% increase in hydrogen production rate under a 4 MW power imbalance, and more stable operation under a 16 MW power imbalance.

Why do flowers wilt?

Resources salvaged when flowers wilt on a perennial plant could promote reproduction by, in preference order, the same flowers (Hypothesis 1), adjacent flowers on the same plant (Hypothesis 2), or during the next flowering season by the same plant (Hypothesis 3). We tested the above hypotheses for Blandfordia grandiflora, a perennial species, where some plants included flowers that were allowed to wilt, while equivalent flowers on other plants were prevented from wilting. The abilities of these plants to produce seed were determined by liberally pollinating all flowers. To test Hypotheses 1 and 2, seed set per flower and per plant were compared between plants with and without wilting flowers. To specifically test Hypothesis 3, reproduction was prevented in all flowers. For each experiment, flowering was monitored in the same plants during the next flowering season, thus also enabling Hypothesis 3 to be tested. The results were consistent with Hypothesis 3, but not with Hypotheses 1 and 2. Hence, we verified, for the first time, that plants may benefit from salvaging resources from wilting flowers and re-using these resources for subsequent reproduction. However, contrary to expectations, plants re-used these resources to promote reproduction during subsequent flowering, and not during current flowering by either the same flowers or other flowers on the same plant. The plants must have transferred resources from wilting flowers to underground corms and roots, which provided resources necessary for subsequent flowering. This is likely part of a general plant strategy to salvage resources invested in reproduction during one flowering season and reuse these resources during subsequent flowering.

Invasion stage and competition intensity co-drive reproductive strategies of native and invasive saltmarsh plants: Evidence from field data

Biological invasion poses a significant threat to biodiversity conservation and also results in substantial economic loss including the excessive cost of management to control it. Still, its impact on plant sexual reproduction strategies remains underexplored in natural settings. We conducted a field experiment on native Phragmites australis and invasive Spartina alterniflora in Bohai Bay and assessed plant size (aboveground biomass and height) and sexual reproduction (ear biomass, reproductive allocation, etc.) in conjunction with water and soil properties. The results showed that during the early stage of invasion, the two species declined in size and sexual reproduction, with S. alterniflora showing a lesser decline than P. australis. However, in the late stage of invasion, S. alterniflora maintained its plant size by reducing its investment in sexual reproduction. Moreover, significant reproductive allometries were demonstrated by S. alterniflora under different competition intensities. P. australis displayed heightened sensitivity to water properties and soil non-resource conditions, while S. alterniflora adapted its inherent traits and environmental tolerance. S. alterniflora allocated more resources to thriving as an individual, while P. australis prioritized reproduction by increasing seed production. Overall, this study revealed the reproductive strategies that invasive and native species employ in response to competition and environmental factors, thereby offering crucial insights for conservation and management efforts.

Analysis of non-uniform grid-forming control techniques for the HVDC connection of renewable energy

Grid-forming control techniques are required to achieve a high penetration of power electronics-based renewable energy sources for decarbonized electric systems. However, different grid-forming control strategies can operate in the same grid, and grid stability shall be warranted. This paper analyses the use of different grid-forming control strategies for diode rectifier-based wind power plants. The analysed grid-forming controllers are the well-known droop control, an advanced droop control, and the virtual synchronous machine control. The controllers’ analysis validates the three grid-forming controllers’ interoperability, identifying each control parameter’s contribution to the stability of each system state variable. Furthermore, the analysis allows a better tuning of the control parameters. Additionally, a fault-ride-through strategy that improves the system restoration after faults is proposed and validated. The proposed fault-ride-through strategy achieves a soft restoration of the active power.

Impact of High-density Planting Spacing on Physiological Traits of Cocoa Grown under Coconut Trees

High-density planting (HDP) maximizes land productivity. Optimizing cocoa spacing under coconut trees enhances physiological traits and yield potential. Despite cocoa's integration into coconut agroforestry, spacing's impact on cocoa physiology is unclear. Studying this influence provides insights for better planting strategies, improved crop performance, and sustainable cocoa production in agroforestry settings. The experiment was conducted at the Coconut Farm of the Horticultural College and Research Institute, Tamil Nadu Agricultural University, Coimbatore. Employing a Randomized Block Design (RBD) with eight treatments replicated three times, the study aimed to explore how different spacing levels influence physiological traits in cocoa cultivation. The treatments involved in the experiment included a double row of cocoa planted between two rows of coconut trees, with spacing configurations as follows: T1 (3m x 1.2m), T2 (3m x 2m), T3 (3m x 2.5m), and T4 (3m x 3m). Additionally, a single row of cocoa between two coconut rows was examined, with spacings represented by T5 (1.5m), T6 (2m), T7 (2.5m), and T8 (3m). Results indicated distinct patterns among spacing treatments, with significant differences observed in various physiological characteristics. Notably, T1 (3m x 1.2m) demonstrated the highest leaf area (462.71cm2) and leaf area index (4.85), while T8 (3m) exhibited the highest light interception (74.12%). Additionally, T3 (3m x 2.5m) showcased the highest chlorophyll index (40.52) in cocoa leaves. These findings underscore the importance of spacing configurations in influencing key physiological parameters in cocoa cultivation, providing valuable insights for optimizing planting practices.

Thermal priming of Saccharina latissima: a promising strategy to improve seaweed production and restoration in future climates

Saccharina latissima is a brown algal kelp species of ecological and economic importance. As the rise in sea surface temperature will threaten not only wild populations of S. latissima but also the productivity of kelp farms, crop enhancement techniques will become crucial to mitigate this threat. Priming is a common strategy in crop plants, in which seeds are pre-exposed to moderate stress to improve the performance and tolerance of plants when exposed to harsher conditions. We investigated the potential of thermal priming to improve growth and tolerance of S. latissima. Kelp gametophytes primed at 20°C for 2, 4 and 6 wk and then re-transferred to 5°C were compared to a naïve treatment maintained at 5°C. Gametophyte priming increased growth of subsequently formed sporophytes by up to 30% (for 4 wk priming) compared to the naïve treatment. Female gametophyte growth in the priming environment was positively correlated to offspring sporophyte growth, indicating a maternal effect. Sporophytes were exposed to heat stress of 20°, 22°, 23° and 24°C for 2 wk. Sporophytes from 4 and 6 wk primed gametophytes exhibited 11 d longer tolerance at 22°C, 7 d longer tolerance at 23°C and 1°C higher thermal tolerance over 7 d compared to naïve sporophytes and sporophytes from 2 wk priming. A priming time of 4 wk was optimal for both sporophyte growth and thermal tolerance. Our results suggest that priming is a promising crop enhancement technique that could improve yield for seaweed farmers and restoration of kelp forests threatened by warming climates.

Machine Learning for Advanced Emission Monitoring and Reduction Strategies in Fossil Fuel Power Plants

Fossil fuel power plants are a significant contributor to global carbon dioxide (CO2) and nitrogen oxide (NOx) emissions. Accurate monitoring and effective reduction of these emissions are crucial for mitigating climate change. This systematic review examines the current state of research on the application of machine learning techniques in evaluating the emissions from fossil fuel power plants. This review first briefly introduces the continuous emission monitoring (CEM) systems and predictive emission monitoring (PEM) systems that are commonly used in power plants and highlights that machine learning models can significantly improve PEM systems through their capability to process and interpret large datasets intelligently to transform traditional emission monitoring systems by enhancing their precision, effectiveness, and cost-efficiency. Compared to previously published review articles, the key contribution and innovation in this present review is the discussion of machine learning models in CO2/NOx emissions according to the different algorithms used, including their advantages and disadvantages in a systematic way, which aims to help future researchers to develop more effective machine learning models. The most popular machine learning model includes reinforcement learning, a forward neural network, a long short-term memory neural network, and support vector regression. While each model method has its own advantages and disadvantages, we noted that training data quality, as well as the proper selection of model parameters, plays an important role. The challenges and research gaps, such as model transferability, a deep understanding of the physics of CO2/NOx emissions, and the availability of high-quality data for training machine learning models, are identified, and recommendations as well as potential future research directions to address these challenges are proposed and discussed.