Membership Function Values Research Articles

Combining GWAS and RNA-Seq approaches identifies the FtADH1 gene for drought resistance in Tartary buckwheat

Drought is one of the major environmental constraints that significantly affects seedling emergence, yield, and quality of Tartary buckwheat, thereby hindering the development of its industry. However, the molecular mechanisms underlying drought tolerance genes in Tartary buckwheat remain largely unexplored. Alcohol dehydrogenase (ADH), one of the essential plant proteins, plays a crucial role in growth, development, and stress responses, but its specific role in drought resistance is still unclear. In this study, we identified an ADH gene FtADH1, using a membership function value of drought tolerance (MFVD) combined with a genome-wide association study (GWAS) and transcriptomic profiles that confers drought tolerance in Tartary buckwheat. Our findings demonstrated that the overexpression of FtADH1 in Arabidopsis and Tartary buckwheat hairy roots enhances drought tolerance by promoting root elongation and mitigating elevated levels of reactive oxygen species (ROS). Our findings demonstrated that FtADH1 can enhanced tolerance to drought stresses in both Tartary buckwheat and Arabidopsis. This study identifies the FtADH1 as a new player in affecting ROS level and the stress response of Tartary buckwheat by regulating protective enzyme activities at a high level to scavenge ROS and modulating root growth under drought stress. Further, we identified proteins interacting with FtADH1 through a prokaryotic expression pull-down assay combined with mass spectrometry, revealing that FtADH1 specifically interacts with the S-adenosyl-L-methionine (SAM) synthetase protein, FtSAMS1. Overexpression of FtSAMS1 was found to enhance ADH enzymatic activity, leading to increased SAM content in overexpressing Tartary buckwheat hairy roots under water-deficit conditions. Additionally, FtSAMS1 overexpression induced a drought-resistant phenotype in Arabidopsis and Tartary buckwheat hairy roots under drought stress, revealing the biological function of FtADH1. Evolutionary analysis indicates that ADH1 in Fagopyrum species has undergone significant evolutionary events, including duplication and purifying selection, which may contribute to functional diversification and adaptive advantages such as drought resistance in cultivated buckwheat. In summary, this study proposes that FtADH1 is a key contributor to drought tolerance, and its interaction with FtSAMS1 holds potential for the development of drought-resistant varieties in Tartary buckwheat and its relative species.

Evaluation of prototyping methods for interactive virtual systems based on fuzzy preference relation

The object of this study is the process of determining the optimal method for prototyping interactive virtual systems using the principle of multi-criteria optimization. The paper addresses the task to design tools for evaluating prototyping methods and introduces software developed to identify the best option according to priority criteria. The optimal prototyping method was chosen based on the maximum value of the membership function with indicators of 0.3, 0.7, and 1. Thus, the best method for the given task is rapid prototyping, with a membership function value equal to 1. These results have made it possible to solve the problem by evaluating fuzzy preference relations on a set of alternatives. Additionally, a relation convolution was constructed, from which a subset of non-dominated alternatives was identified. The key features of the proposed approach are obtaining clear quantitative evaluation parameters by processing descriptive, non-unified, and non-formalized input data. The fuzzy preference relations for the set of alternatives are explained by the analysis of the most common methods for prototyping interactive systems. The developed information system could be used for managerial decision-making regarding the selection of optimal prototyping option from several possibilities, by comparing methods based on predefined criteria with arbitrary weighting coefficients. The result of the research is a universal system adaptable to specific user tasks. The resulting data will contribute to improving the efficiency of prototyping and, consequently, reducing costs.

Optimal Light Intensity for Lettuce Growth, Quality, and Photosynthesis in Plant Factories.

In agriculture, one of the most crucial elements for sustained plant production is light. Artificial lighting can meet the specific light requirements of various plants. However, it is a challenge to find optimal lighting schemes that can facilitate a balance of plant growth and nutritional qualities. In this study, we experimented with the light intensity required for plant growth and nutrient elements. We designed three light intensity treatments, 180 μmol m-2 s-1 (L1), 210 μmol m-2 s-1 (L2), and 240 μmol m-2 s-1 (L3), to investigate the effect of light intensity on lettuce growth and quality. It can be clearly seen from the radar charts that L2 significantly affected the plant height, fresh weight, dry weight, and leaf area. L3 mainly affected the canopy diameter and root shoot ratio. The effect of L1 on lettuce phenotype was not significant compared with that of the others. The total soluble sugar, vitamin C, nitrate, and free amino acid in lettuce showed more significant increases under the L2 treatment than under the other treatments. In addition, the transpiration rate and stomatal conductance were opposite to each other. The comprehensive evaluation of the membership function value method and heatmap analysis showed that lettuce had the highest membership function value in L2 light intensity conditions, indicating that the lettuce grown under this light intensity could obtain higher yield and better quality. This study provides a new insight into finding the best environmental factors to balance plant nutrition and growth.

Winter forage crops influence soil properties through establishing different arbuscular mycorrhizal fungi communities in paddy field

Winter planting is promising for improving the utilization rate of fallow paddy fields in southern China by establishing arbuscular mycorrhizal fungi (AMF) communities. However, the effects of different winter forage crops on AMF community construction remain unknown. The AMF community establishment of different winter planting forage crops were conducted in oat, rye, Chinese milk vetch, and ryegrass, with winter fallow as a control. The AMF colonization rate, soil AMF spore density, community structure and diversity, and soil physicochemical properties were determined. The results showed that the total nitrogen and available nitrogen in winter Chinese milk vetch were 11.11% and 16.92% higher than those in winter fallow (P < 0.05). After planting winter forage crops, the AMF spore density in winter oat, rye, Chinese milk vetch, and ryegrass soil were 127.90%, 64.37%, 59.91%, and 73.62% higher than that before planting, respectively (P < 0.05). Claroideoglomus was the dominant AMF genus in the soil of winter planting oat, rye, and ryegrass. The average membership function value of winter Chinese milk vetch was the highest, indicating that it had the best comprehensive effect on soil physicochemical properties, AMF community structure and diversity, and fresh forage yield. Winter forage crops could increase the spore pool of soil AMF and improve the soil AMF community structure and diversity. Winter Chinese milk vetch in paddy field had the best comprehensive effect on soil physicochemical properties and soil AMF community according to the comprehensive evaluation. These findings provide a theoretical basis for sustainable development and utilization of the southern rice paddy ecosystem.

Accelerated mustard [Brassica juncea (L.) Czern &amp; Coss] improvement for salt tolerance: Photosynthetic traits and selection indices

AbstractPhotosynthetic traits can sense and signal abiotic stress at early growth stages of mustard [Brassica juncea (L.) Czern &amp; Coss]. The integration stress responses with multiple selection indices can select the fittest/tolerant genotypes. With this goal, a set of 210 diverse mustard genotypes were phenotyped under control and salt‐stress (electrical conductivity = 12 dS m−1). Significant dynamic response of the plant to salt stress with reduction for all morpho‐physiological traits and genotype × treatment interaction was observed under salinity. A higher accumulation of Na+ was recorded in the roots, followed by shoots. A close correspondence between phenotypic coefficient of variation and genotypic coefficient of variation under control and salt stress conditions results in high heritability (h2) in broad sense. A significant negative association between shoot and root Na+ content and rate of photosynthesis also indicated that Na+ was significantly restricted in the root. This was not found to be efficient because considerable Na+ buildup was observed in the shoot. Multiple trait‐based indices, such as membership function value of salinity tolerance, classical Smith–Hazel index, factor analysis ideotype–best linear unbiased prediction index, and multi‐trait genotype‐ideotype distance index, deduced CS 2009‐159, CS 2009‐420, CS 2009‐124, and Swarn Jyoti (RH‐9801) are promising for identifying salt‐tolerant genotypes that can serve as donors for the development of salt‐tolerant cultivars.

Особенности оценки вредоносной активности в инфраструктуре Умного города на основе гранулирования информации и гранулярных моделей вычислений

The object of the research is a new methodological approach to information granulation and fuzzy granular calculations, as a mathematical and methodological tool for improving the reliability of assessing the level of information security of the Smart City infrastructure. The proposed approach is one of the options for the practical application of elements of the theory of fuzzy sets in the tasks of search, identification and current assessment of signs of time-bearing activity. A detailed analysis of the features of this approach has been carried out, determining the expediency and conditions of its application for assessing malicious activity in the infrastructure of a Smart City. The theoretical aspects of the application of information granulation and fuzzy granular computing to the assessment of malicious activity combining various signs for various categories of potential threats to the infrastructure and subjects of a Smart City - the categories “cyberattack”, “malicious virus threat” or “data leakage (loss)” are studied and described. The analysis of the features of the proposed approach is carried out, which allows taking into account the opinions of experts and eliminating the vagueness associated with noise, disorder and lack of formalization of surveillance data collected and pre-processed in the interests of assessing threats and consequences of negative manifestations of malicious activity. A sequence of calculations and analytical expressions for calculating the estimated values of signs of harmful activity for various categories of potential threats to the infrastructure and subjects of a Smart City has been developed and described in detail. The approach assumes the practical possibility of evaluating signs of malicious activity using information granules formed on the basis of a minimum numerical distance between the values of membership functions characterizing vaguely specified data on the presence or absence of observed signs (attributes) of malicious activity, as well as granular summation and determination of the trace function of the granular sum. At the same time, the proposed approach makes it possible to obtain estimates of signs of malicious activity that are adequate to the tasks of monitoring the Smart City security policy and, ultimately, provides increased reliability of proactive threat control and analysis of the possible consequences of a negative manifestation of suspicious activity.

Dissecting the Superior Drivers for the Simultaneous Improvement of Fiber Quality and Yield Under Drought Stress Via Genome-Wide Artificial Introgressions of Gossypium barbadense into Gossypium hirsutum.

Global water scarcity and extreme weather intensify drought stress, significantly reducing cotton yield and quality worldwide. Drought treatments are conducted using a population of chromosome segment substitution lines generated from E22 (G. hirsutum) and 3-79 (G. barbadense) as parental lines either show superior yields or fiber quality under both control and drought conditions. Fourteen datasets, covering 4 yields and 4 quality traits, are compiled and assessed for drought resistance using the drought resistance coefficient (DRC) and membership function value of drought resistance (MFVD). Genome-wide association studies, linkage analysis, and bulked segregant analysis are combined to analyze the DR-related QTL. A total of 121 significant QTL are identified by DRC and MFVD of the 8 traits. CRISPR/Cas9 and virus-induced gene silencing techniques verified DRR1 and DRT1 as pivotal genes in regulating drought resistant of cotton, with hap3-79 exhibiting greater drought resistance than hapE22 concerning DRR1 and DRT1. Moreover, 14 markers with superior yield and fiber quality are selected for drought treatment. This study offers valuable insights into yield and fiber quality variations between G. hirsutum and G. barbadense amid drought, providing crucial theoretical and technological backing for developing cotton varieties resilient to drought, with high yield and superior fiber quality.

Multi objective optimization approach for multi-item inventory control: A case study in leather industry

PT ASA is a leather tanning company. Almost 65% of the company's assets are allocated for the procurement of raw leather, which consists of goat and sheepskin. In addition to being expensive, the availability of raw leather is also very limited. The company faces a trade-off where, on one hand, the raw material is easily decayed, but on the other hand, its availability is extremely limited, and if there is not enough inventory, the production process will be disrupted. In this research, a multi-objective optimization model is developed for controlling the inventory of raw leather using the Fuzzy Goal Programming approach. The objectives to be achieved are to minimize the total inventory cost, maximize the total quantity of raw leather that meets standards, and minimize the total cost of losses due to decayed raw leather. Based on the calculations, the fuzzy goal programming membership function value is obtained at 0.9155, with a total inventory cost over the planning horizon of IDR 10,341,630,000, a total of 1,279,542 sq ft of raw leather meeting standards, and a total loss cost due to decayed raw leather of IDR 142,911,691.

Physiological Response to Low-Temperature Stress and Cold Resistance Evaluation of Ziziphus jujuba var. spinosa Clones from Different Provenances

To investigate the low-temperature adaptability of different provenances of Ziziphus jujuba var. spinosa, we used 21 clones from seven provenances as experimental materials and observed the changes in physiological and biochemical indicators and the characteristics of anatomical structures under low-temperature stress. A comprehensive evaluation of their cold resistance was conducted using the membership function method. As the temperature decreased, the relative electrical conductivity (REC) of clone 89 became stable and had the lowest LT50 value (−44.04 °C). The cold-resistant Z. jujuba var. spinosa had a higher bound water/free water (BW/FW) ratio and antioxidant enzyme activity and accumulated large quantities of osmotic regulatory substances. Higher xylem, phloem, and xylem–cortex ratios and greater conduit density enhanced the cold resistance of Z. jujuba var. spinosa. The membership function values of clones 89, 90, 91, 604, and 612 were greater than 0.6, indicating that they could be evaluated as resources with the potential for low-temperature resistance. The cold resistance rankings for the different provenances were as follows: Kazuo, Liaoning &gt; Jiaxian, Shaanxi &gt; Fuxing, Heibei &gt; Changqing, Shandong &gt; Neiqiu, Heibei &gt; Yanchuan, Shaanxi &gt; Xiaxian, Shanxi. These results provide a scientific basis for the rapid and accurate identification of cold resistance in Z. jujuba var. spinosa resources and the breeding and cultivation of new cold-resistant varieties of this subspecies.

A method for screening salt stress tolerance in Indian mustard (Brassica juncea) (L.) Czern & Coss at seedling stage

Fifty-nine diverse Brassica juncea (Indian mustard) genotypes were used to find an effective screening method to identify salt tolerance at the germination and seedling stages. Salinity stress limits crop productivity and is difficult to simulate on farms, hindering parental selection for hybridization programmes and the development of tolerant cultivars. To estimate an optimum salt concentration for screening, seeds of 15 genotypes were selected randomly and grown in vitro at 0 mM/L, 75 mM/L, 150 mM/L, 225 mM/L, and 300 mM/L concentrations of NaCl in 2 replications in a complete randomized design. Various morphological parameters, viz., length of seedling, root and shoot length, fresh weight, and dry weight, were observed to determine a single concentration using the Salt Injury Index. Then, this optimum concentration (225 mM/L) was used to assess the salt tolerance of all the 59 genotypes in 4 replications while observing the same morphological parameters. With the help of Mean Membership Function Value evaluation criteria, the genotypes were categorized into 5 grades: 4 highly salt-tolerant (HST), 6 salt-tolerant (ST), 19 moderately salt-tolerant (MST), 21 salt-sensitive (SS), and 9 highly salt-sensitive (HSS). Seedling fresh weight (SFW) at 225 mM/L was found to be an ideal trait, which demonstrates the extent to which B. juncea genotypes respond to saline conditions. This is the first report that establishes a highly efficient and reliable method for evaluating the salinity tolerance of Indian mustard at the seedling stage and will facilitate breeders in the development of salt-tolerant cultivars.

Assessment of salt tolerance in Algerian oasis wheat landraces: An examination of biochemical, physiological, and agronomical traits

Wheat landraces cultivated in the oases of Algeria are are know for their resistance to abiotic stresses as a result of the extreme environmental constraints of the Sahara. As such, these landraces represent valuable breeding material for improving abiotic stress tolerance in wheat. This study was conducted to evaluate salt tolerance of bread and durum wheat from the Algerian oases. Ten wheat landraces from the Algerian oases were grown under prolonged salinity stress (150 mM NaCl) in greenhouse conditions. Data were assessed for 19 physiological, biochemical, and agronomical traits. The wheat landraces exhibited considerable variation in their salinity stress tolerance. The membership function value of salt tolerance identified Oum RokbaElhamra, Khellouf and Zeghlou as the most tolerant landraces while Bourione was identified as sensitive. The salt-tolerant and moderately tolerant wheat landraces maintained stable yields under conditions of salinity stress. Regression models constructed from MFVS and salt tolerance coefficients showed that for bread wheat, amino acid content and grain yield accounted for most of the variation in MFVS, while for durum wheat, the number of grains per plant and Na+ content explained the majority of observed differences in MFVS. Correlation analysis showed that the MFVS was significantly associated with grain yield, selectivity between K+ and Na+, and plant height. The results confirm that Algerian oasis wheat landraces are a valuable source given their salt tolerance and could be utilized in breeding programs seeking to improve salinity stress resilience in wheat.

Evaluation of Cordyceps sinensis Quality in 15 Production Areas Using Metabolomics and the Membership Function Method.

Cordyceps sinensis is a precious medicinal and edible fungus, which is widely used in body health care and disease prevention. The current research focuses on the comparison of metabolite characteristics between a small number of samples and lacks a comprehensive evaluation of the quality of C. sinensis in a large-scale space. In this study, LC-MS/MS, principal component analysis (PCA), hierarchical cluster analysis (HCA), and the membership function method were used to comprehensively evaluate the characteristics and quality of metabolites in 15 main producing areas of C. sinensis in China. The results showed that a total of 130 categories, 14 supercategories, and 1718 metabolites were identified. Carboxylic acids and derivatives, fatty acyls, organo-oxygen compounds, benzene and substituted derivatives, prenol lipids, and glycerophospholipids were the main components of C. sinensis. The HCA analysis and KEGG pathway enrichment analysis of 559 differentially accumulated metabolites (DAMs) showed that the accumulation models of fatty acids and conjugates and carbohydrates and carbohydrate conjugates in glycerophospholipid metabolism and arginine and proline metabolism may be one of the reasons for the quality differences in C. sinensis in different producing areas. In addition, a total of 18 biomarkers were identified and validated, which had a significant discrimination effect on the samples (p < 0.05). Overall, YS, BR, and ZD, with the highest membership function values, are rich and balanced in nutrients. They are excellent raw materials for the development of functional foods and provide scientific guidance for consumers to nourish health care.

ISSR-Assisted Breeding of Excellent New Strains of Ganoderma lingzhi through Single-Spore Selfing

ISSR-Assisted Breeding of Excellent New Strains of Ganoderma lingzhi through Single-Spore Selfing

Robust Fuzzy Neural Network With an Adaptive Inference Engine.

Fuzzy neural networks (FNNs) have been very successful at handling uncertainty in data using fuzzy mappings and if-then rules. However, they suffer from generalization and dimensionality issues. Although deep neural networks (DNNs) represent a step toward processing high-dimensional data, their capacity to address data uncertainty is limited. Furthermore, deep learning algorithms designed to improve robustness are either time consuming or yield unsatisfactory performance. In this article, we propose a robust fuzzy neural network (RFNN) to overcome these problems. The network contains an adaptive inference engine that is capable of handling samples with high-level uncertainty and high dimensions. Unlike traditional FNNs that use a fuzzy AND operation to calculate the firing strength for each rule, our inference engine is able to learn the firing strength adaptively. It also further processes the uncertainty in membership function values. Taking advantage of the learning ability of neural networks, the acquired fuzzy sets can be learned from training inputs automatically to cover the input space well. Furthermore, the consequent layer uses neural network structures to enhance the reasoning ability of the fuzzy rules when dealing with complex inputs. Experiments on a range of datasets show that RFNN delivers state-of-the-art accuracy even at very high levels of uncertainty. Our code is available online. https://github.com/leijiezhang/RFNN.

Root system architecture change in response to waterlogging stress in a 448 global collection of rapeseeds (Brassica napus L.).

A novel image-based screening method for precisely identifying genotypic variations in rapeseed RSA under waterlogging stress was developed. Five key root traits were confirmed as good indicators of waterlogging and might be employed in breeding, particularly when using the MFVW approach. Waterlogging is a vital environmental factor that has detrimental effects on the growth and development of rapeseed (Brassica napus L.). Plant roots suffer from hypoxia under waterlogging, which ultimately confers yield penalty. Therefore, it is crucially important to understand the genetic variation of root system architecture (RSA) in response to waterlogging stress to guide the selection of new tolerant cultivars with favorable roots. This research was conducted to investigate RSA traits using image-based screening techniques to better understand how RSA changes over time during waterlogging at the seedling stage. First, we performed a t-test by comparing the relative root trait value between four tolerant and four sensitive accessions. The most important root characteristics associated with waterlogging tolerance at 12 h are total root length (TRL), total root surface area (TRSA), total root volume (TRV), total number of tips (TNT), and total number of forks (TNF). The root structures of 448 rapeseed accessions with or without waterlogging showed notable genetic diversity, and all traits were generally restrained under waterlogging conditions, except for the total root average diameter. Additionally, according to the evaluation and integration analysis of 448 accessions, we identified that five traits, TRL, TRSA, TRV, TNT, and TNF, were the most reliable traits for screening waterlogging-tolerant accessions. Using analysis of the membership function value (MFVW) and D-value of the five selected traits, 25 extremely waterlogging-tolerant materials were screened out. Waterlogging significantly reduced RSA, inhibiting root growth compared to the control. Additionally, waterlogging increased lipid peroxidation, accompanied by a decrease in the activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT). This study effectively improves our understanding of the response of RSA to waterlogging. The image-based screening method developed in this study provides a new scientific guidance for quickly examining the basic RSA changes and precisely predicting waterlogging-tolerant rapeseed germplasms, thus expanding the genetic diversity of waterlogging-tolerant rapeseed germplasm available for breeding.

Salt tolerance evaluation and mini-core collection development in Miscanthus sacchariflorus and M. lutarioriparius.

Marginal lands, such as those with saline soils, have potential as alternative resources for cultivating dedicated biomass crops used in the production of renewable energy and chemicals. Optimum utilization of marginal lands can not only alleviate the competition for arable land use with primary food crops, but also contribute to bioenergy products and soil improvement. Miscanthus sacchariflorus and M. lutarioriparius are prominent perennial plants suitable for sustainable bioenergy production in saline soils. However, their responses to salt stress remain largely unexplored. In this study, we utilized 318 genotypes of M. sacchariflorus and M. lutarioriparius to assess their salt tolerance levels under 150 mM NaCl using 14 traits, and subsequently established a mini-core elite collection for salt tolerance. Our results revealed substantial variation in salt tolerance among the evaluated genotypes. Salt-tolerant genotypes exhibited significantly lower Na+ content, and K+ content was positively correlated with Na+ content. Interestingly, a few genotypes with higher Na+ levels in shoots showed improved shoot growth characteristics. This observation suggests that M. sacchariflorus and M. lutarioriparius adapt to salt stress by regulating ion homeostasis, primarily through enhanced K+ uptake, shoot Na+ exclusion, and Na+ sequestration in shoot vacuoles. To evaluate salt tolerance comprehensively, we developed an assessment value (D value) based on the membership function values of the 14 traits. We identified three highly salt-tolerant, 50 salt-tolerant, 127 moderately salt-tolerant, 117 salt-sensitive, and 21 highly salt-sensitive genotypes at the seedling stage by employing the D value. A mathematical evaluation model for salt tolerance was established for M. sacchariflorus and M. lutarioriparius at the seedling stage. Notably, the mini-core collection containing 64 genotypes developed using the Core Hunter algorithm effectively represented the overall variability of the entire collection. This mini-core collection serves as a valuable gene pool for future in-depth investigations of salt tolerance mechanisms in Miscanthus.

A comprehensive approach for evaluating salinity stress tolerance in brinjal (Solanum melongena L.) germplasm using membership function value.

Salinity is a major stress factor affecting plant growth and development, which limits the productivity of vegetable crops. Brinjal (Solanum melongena L.), an important vegetable cultivated across the globe is susceptible to salinity stress. In the present study, the salinity tolerance response of 110 brinjal germplasm lines was evaluated at the germination stage using the membership function value (MFV). The MFV is a comprehensive index that integrates salt-tolerance indices of germination parameters. The brinjal germplasms were classified into highly salt-tolerant (>0.79), salt-tolerant (0.79-0.65), moderately salt-tolerant (0.64-0.21), salt-sensitive (0.20-0.07), and highly salt-sensitive (<0.07) based on their mean MFV. Among all the traits examined, germination percentage (0.874) and vigour index-I (0.808) were the most reliable traits for assessing salinity tolerance, showing a higher correlation with mean MFV. Furthermore, a comprehensive mathematical model was developed for evaluating the salt-tolerance of the brinjal germplasm. We validated our model by evaluating the brinjal germplasm at the seedling stage through a hydroponic experiment, and a strong positive correlation was observed between growth parameters at the germination and seedling stages. Salt-tolerant genotypes showed higher chlorophyll content, photosynthetic performance and biomass accumulation with lower canopy temperature (1.57°C) under salinity compared to susceptible genotypes (2.62°C). These findings provide valuable insights into the salinity tolerance of the brinjal germplasm, and identified potential candidates to elucidate the molecular mechanisms and develop salinity-tolerant cultivars. To our knowledge, this is the first report using a mathematical model based on MFV to evaluate the salt-tolerance of any vegetable crop.

Effects of different colors of plastic-film mulching on soil temperature, yield, and metabolites in Platostoma palustre

Platostoma palustre is an annual herb and an important medicinal and edible plant in southern China. Plastic-film mulching is an effective agronomic practice in the cultivation system of P. palustre, of which black-film mulching is the most common. However, fewer researches have been focused on the use of other colors of plastic films in P. palustre cultivation. In this study, different colors (white, black, red, and green) of plastic film were adopted, and the effects of different colors of plastic film mulching on the soil temperature, yield, and metabolites of P. palustre were investigated. The results showed that the fresh weight of a single plant of the green film treatment was significantly higher than that of the white film treatment (n = top 28). Based on the results of three temperature measurements, the soil temperature was almost the highest in the red film treatment and lowest in the white film treatment. The metabolomic analysis revealed that a total of 103 differential metabolites were identified. Among these, the gluconic acid, deoxyribose, and N-Acetylmannosamine in the red film treatment presented the highest abundance compared with the other treatments, meanwhile, the abundances of the five monosaccharides in the red film treatment were significantly higher than those of the green film treatment. Moreover, the sucrose, trehalose, and D-(+)-trehalose in the green film treatment exhibited the highest abundance, and the abundances of eight different amino acids in the red film treatment were almost the lowest while those in the black film treatment were almost the highest. Further analysis of the membership function values indicated that the black and red film treatments might be more suitable for the cultivation and quality production of P. palustre in comparison with the other two treatments. This study will provide a theoretical basis for improving the efficient cultivation technology of P. palustre and forming a theoretical system of P. palustre film mulching cultivation.

Application of Improved PSO and BP Hybrid Optimization Algorithm in Electrical Automation Intelligent Control

A fuzzy RBF-PID control strategy based on particle swarm optimization (PSO) algorithm is proposed to solve the problem of large inertia lag in temperature control system of industrial production refuse furnace. In this control system, an improved particle swarm optimization algorithm combined with inertia weight and genetic transformation was used to optimize the initial values of membership functions of fuzzy RBF (radial basis function). Then, BP (error backpropagation) algorithm is used for fine tuning, and fuzzy reasoning and RBF learning ability are combined to adjust the PID control parameters online to achieve the optimal PID control effect. The simulation results show that the algorithm has fast tracking, small overshoot, and is not easily trapped in local minima. At the same time, its robustness and anti-interference performance are better than traditional PID control.

Exploration of ecological benefits of Conyza Canadensis — analysis and evaluation of the effects of Conyza Canadensis water extract, ascorbic acid, and gibberellin on the salt tolerance to several crops at seed germination

Soil salinity poses a major threat to plant growth and food security. Seed germination are more sensitive to salinity stress. In this study, sorghum, wheat, tomato and peanut were used to analyze and evaluate (using the membership function method) the salt tolerance at the germination with three exogenous substances - Conyza Canadensis water extract (CCE), ascorbic acid (AsA), and gibberellin (GA3). At the germination stage, the germination rate, the germination energy and the germination vigor index are measured and the membership function values (MFVs) are calculated. After salt-tolerant analysis and evaluation, the sequence of salt tolerance is sorghum ≈ wheat &gt; tomato &gt; peanut. AsA and GA3 can significantly improve the salt tolerance of crops(P&lt;0.05). Interestingly, CCE can also improve the salt tolerance in germination stage (sorghum, wheat and tomato) (P&lt;0.05). Therefore, Conyza Canadensis has shown ecological benefits in improving the salt tolerance of some crops.