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Genomic selection (GS) can accomplish breeding faster than phenotypic selection. Improving prediction accuracy is the key to promoting GS. To improve the GS prediction accuracy and stability, we introduce parallel convolution to deep learning for GS and call it a parallel neural network for genomic selection (PNNGS). In PNNGS, information passes through convolutions of different kernel sizes in parallel. The convolutions in each branch are connected with residuals. Four different Lp loss functions train PNNGS. Through experiments, the optimal number of parallel paths for rice, sunflower, wheat, and maize is found to be 4, 6, 4, and 3, respectively. Phenotype prediction is performed on 24 cases through ridge-regression best linear unbiased prediction (RRBLUP), random forests (RF), support vector regression (SVR), deep neural network genomic prediction (DNNGP), and PNNGS. Serial DNNGP and parallel PNNGS outperform the other three algorithms. On average, PNNGS prediction accuracy is 0.031 larger than DNNGP prediction accuracy, indicating that parallelism can improve the GS model. Plants are divided into clusters through principal component analysis (PCA) and K-means clustering algorithms. The sample sizes of different clusters vary greatly, indicating that this is unbalanced data. Through stratified sampling, the prediction stability and accuracy of PNNGS are improved. When the training samples are reduced in small clusters, the prediction accuracy of PNNGS decreases significantly. Increasing the sample size of small clusters is critical to improving the prediction accuracy of GS. [ABSTRACT FROM AUTHOR]

Influence maximization in online social networks is used to select a set of influential seed nodes to maximize the influence spread under a given diffusion model. However, most existing proposals have huge computational costs and only consider the dyadic influence relationship between two nodes, ignoring the higher-order influence relationships among multiple nodes. It limits the applicability and accuracy of existing influence diffusion models in real complex online social networks. To this end, in this paper, we present a novel information diffusion model by introducing hypergraph theory to determine the most influential nodes by jointly considering adjacent influence and higher-order influence relationships to improve diffusion efficiency. We mathematically formulate the influence maximization problem under higher-order influence relationships in online social networks. We further propose a hypergraph sampling greedy algorithm (HSGA) to effectively select the most influential seed nodes. In the HSGA, a random walk-based influence diffusion method and a Monte Carlo-based influence approximation method are devised to achieve fast approximation and calculation of node influences. We conduct simulation experiments on six real datasets for performance evaluations. Simulation results demonstrate the effectiveness and efficiency of the HSGA, and the HSGA has a lower computational cost and higher seed selection accuracy than comparison mechanisms. [ABSTRACT FROM AUTHOR]

Improving the productivity of sweet pepper (Capsicum annuum) is essential to meeting the increasing global demand. This can be partially accomplished by investigating and determining high-yield traits, thereby enabling the selection or breeding of high-yield plants. Therefore, this study aimed to determine the high-yield traits of sweet pepper by analyzing its yield components. We analyzed yield components of commercially available cultivars (red and yellow) that were hydroponically grown in a greenhouse (e.g., total fruit fresh weight, fruit dry weight, fruit dry matter content, total dry matter production, and light-use efficiency) using Pearson's correlation coefficient (r). Our results showed the following: the total fruit fresh weight was positively and negatively correlated with the fruit dry weight (r = 0.83; P < 0.001) and fruit dry matter content (r = -0.70; P < 0.001), respectively; the fruit dry weight was positively correlated with the total dry matter production (r = 0.50; P < 0.01), and the total dry matter production was positively correlated with the light-use efficiency (r = 0.93; P < 0.001); and the cultivars with the high total fruit fresh weight were characterized by the notably low fruit dry matter content and high light-use efficiency (e.g., 'Gialte'). In conclusion, high-yielding sweet peppers are characterized by a low fruit dry matter content and high light-use efficiency. [ABSTRACT FROM AUTHOR]

Pd-based catalysts exhibit higher catalytic activity and durability in many electrochemical reactions. However, the electrochemical performance can be further enhanced by fine-tune of the alloy composition. Although binary alloys have been fully studied, the multicomponent alloys are far beyond understanding, which leaves cocktail effect a compromised explanation for the high-entropy alloy. Herein Pd nanosheet-seeded growth was used to synthesize a Pd-Zn-Cd ternary alloy by accurately controlling the Pd-Zn-Cd molar ratio through adjusting the amount of introduced Cd precursor. Through analysis of the crystal phase structure of PdCdZnx and PdZnxCd1−x, the competitive relationship of Zn and Cd in the alloying process with Pd was unveiled: Pd1Cd1 intermetallics (IMC) is thermodynamically favored over Pd1Zn1 IMC in the ternary system. However, the increased structure stability of PdCd over PdZn does not bring about increased durability in the catalytic ethanol oxidation reaction. The morphology selection of Pd seeds is also crucial for the study, as Pd cubes, Pd tetrahedrons, and Pd octahedrons do not form PdZn in the same protocol. The successful alloying through the seeded growth depends on the maximum diffusion depth of foreign atoms into the seed. [ABSTRACT FROM AUTHOR]

Cardamom is one of the prominent plants with significant economic value in the Banyumas Regency, Central Java, Indonesia. Although Javanese cardamom is traditionally categorized under Amomum compactum, the morphological variations observed create ambiguity about its exact species status. DNA barcoding using the maturase K (matK) and ribulose-bisphosphate carboxylase (rbcL) genes is proven as a reliable technique to elucidate the taxonomic status of morphological variable plant cultivars. This study aimed to characterize cardamom from Banyumas Regency using morphological and molecular approaches for taxonomic status identification and genetic diversity evaluation. The matK and rbcL genes were selected as genetic markers and sequenced using a bidirectional sequencing technique. Morphological examination showed significant color variations at the cardamom stem base. All samples had high genetic identities to reference species in databases and were supported by high query cover and zero e-values. Therefore, molecular characterization, alongside geographic distribution assessment, established that this plant belongs to a single species, Amomum compactum. Additionally, the analysis conducted showed a low level of genetic diversity, as evidenced by haplotype and nucleotide diversity. Low-level genetic diversity provides additional data to convince that cardamon in Banyumas Regency belongs to a single species. These results are essential data in seed selection for further cultivation. [ABSTRACT FROM AUTHOR]

Copyright of Acta Agriculturae Zhejiangensis is the property of Acta Agriculturae Zhejiangensis Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Heat-tolerant species have become increasingly important because of climate change; however, a selection system for woody plants is not well established. This study was conducted to establish a selection system for heat-tolerant woody plant species and to select heat-tolerant species. After selecting heat-tolerant woody plants and applying heat stress to 27 species, the electrolyte leakage index (ELI) was measured. The ELI of the heat-tolerant species was lower than that of the non heat-tolerant species, and they survived well after heat stress treatment. For the preselected species, the degree of cell death was measured by Evans blue staining method, and the heat stress recovery ability was measured by a 3,3′-diaminobenzidine (DAB) staining method. The species showed less cell death even after heat treatment, and oxidative stress was low after recovery from heat stress. Traditional screening methods are mainly performed through field screening; however, this is difficult because it requires many samples and considerable time. The results of this study are relatively rapid, reproducible, and highly sensitive, so it is judged to be a method that can complement the existing traditional method as a heat-tolerant plant selection system. The results of this study can be widely used for the selection and breeding of heat-tolerant plants in preparation for climate change. [ABSTRACT FROM AUTHOR]

Fuzz testing technology is an important approach to detecting SQL injection vulnerabilities. Among them, coverage-guided gray-box fuzz testing technology is the current research focus, and has been proved to be an effective method. However, for SQL injection vulnerability, coverage-guided gray-box fuzz testing as a detection method has the problems of low efficiency and high false positives. In order to solve the above problems, we propose a potentially vulnerable code-guided gray-box fuzz testing technology. Firstly, taint analysis technology is used to locate all the taint propagation paths containing potential vulnerabilities as potentially vulnerable codes. Then, the source code of the application program is instrumented according to the location of the potentially vulnerable code. Finally, the feedback of seeds during the run is used to guide seed selection and seed mutation, and a large number of test cases are generated. Based on the above techniques, we implement the sqlFuzz prototype system, and use this system to analyze eight modern PHP applications. The experimental results show that sqlFuzz can not only detect more SQL injection vulnerabilities than the existing coverage-guided gray box fuzz testing technology, but also significantly improve the efficiency, in terms of time efficiency increased by 80 percent. [ABSTRACT FROM AUTHOR]

This study investigates the morphological changes in grape pips resulting from various charring conditions. Employing high-resolution scanning combined with morphometric measurements for morphological analysis, we aimed to understand the effects of charring on grape pips. Our morphometric analysis demonstrated significant alterations in seed shape above 250 °C. The length–width ratio and the occurrence of cracks notably changed, providing a basis for assessing charring conditions. In addition, applying a machine learning classification method, we determined that accurate classification of grape varieties by the morphometric analysis method is feasible for seeds charred at up to 250 °C and 8 h. Integrating the morphometric changes and temperature ranges suitable for classification, we developed a sorting model for archaeological seeds. By projecting length–width ratios onto a curve calculated from controlled conditions, we estimated charring temperatures. Approximately 50% of archaeological seeds deviated from the model, indicating drastic charring conditions. This sorting model facilitates a stringent selection of seeds fit for classification, enhancing the accuracy of our machine learning-based methodology. In conclusion, combining machine learning with morphometric sorting enables the identification of charred grape seeds suitable for identification by the morphometric method. This comprehensive approach provides a valuable tool for future research for the identification of charred grape seeds found in archaeological contexts, enhancing our understanding of ancient viticulture practices and grape cultivation. [ABSTRACT FROM AUTHOR]

A survey of woody marshland hummocks, of Saw Palmetto (Serenoa repens, Arecaceae) thickets, and of additional Southeast Florida habitats revealed multiple sites with dioecy rates of 50-100%, especially in the Saw Palmetto thickets. These rates exceeded any encountered for woody species in a literature review globally or for Florida. A particularly notable feature of the thicket sites is extreme thick low-elevation palm frond canopy coverage, consistent with the historical perspective that a benefit of dioecy is sexual selection for seed quality where harsh ecological filters limit establishment. [ABSTRACT FROM AUTHOR]

Conspecific adults impose strong negative density‐dependent effects on seed survival nearby parent trees, however, the underlying mechanisms are diversified and remain unclear. In this study, we presented consistent evidence that parent‐scented forest floor masked seed odor, reduced cache recovery rate by scatter‐hoarding animals, and then increased seed dispersal far away from mother trees. Our results showed that seed odors of Korean pine Pinus koraiensis match well with the volatile profile of their forest floor. Moreover, scatter‐hoarding animals selectively transported P. koraiensis seeds toward the areas where seed odor was more contrasting against the background substrate, possibly due to the fact that accumulation of conspecific volatile compounds in caches hindered seed detection by scatter‐hoarding animals. Our study provides insight into the role of leaf litter in directing seed dispersal process, representing a novel mechanism by which P. koraiensis increases selection for seed dispersal far away from the parent tree. [ABSTRACT FROM AUTHOR]

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Identification of the key influencers is one of the most important strategies for initiating any transmission process in a social network. However, many of the current studies on influence transmission concentrate primarily on the progressive dissemination phenomenon. Furthermore, the various methods for selecting key influencers that are being explored so far either depend on the structure of the network or the connections between users. As a result, the topology of the network may affect the functioning of these existing methods. Therefore, this paper presents an LTIRS non-progressive model that works on comprehending the non-progressive influence transmission phenomenon, where an influencer may eventually lose their power to influence for a period and the same influencer may revive and engage in active transmission again in the future. In this work, we also propose a scheme named the "AASN" method where the queueing theory is applied in the LTIRS model to investigate the impact of change in the state of a node and compute its influence capacity. Thus, this method provides an efficient way for selecting the key influencer irrespective of the topological properties of the network. The effectiveness of the proposed method is demonstrated by experimental studies on a few real-world datasets. [ABSTRACT FROM AUTHOR]

This document provides a summary of a special issue on the ornamental industry, which covers a wide range of topics related to plant propagation, defense mechanisms, tissue culture, genetics in urban planning, and emerging trends in the ornamental plant market. The research was conducted by authors from various countries, highlighting the global relevance of the industry. The authors hope that readers will find inspiration for further research in this field. The ornamental industry is a diverse and segmented industry that focuses on growing and marketing plants and cut flowers for decorative purposes. The global flower and ornamental plants market was valued at $27 billion in 2021 and is expected to increase in value by 2029. The industry faces challenges such as limited resources, climate change, sustainable pest and disease management, diversification of supply, and optimization of marketing and distribution. Breeding plays a crucial role in developing new cultivars, and advancements in genomics and gene editing have shown promise. Efficient systems for propagation, cultivation, and marketing are also important for the industry's success. [Extracted from the article]

Coverage-guided fuzzing is one of the most popular approaches to detect bugs in programs. Existing work has shown that coverage metrics are a crucial factor in guiding fuzzing exploration of targets. A fine-grained coverage metric can help fuzzing to detect more bugs and trigger more execution states. Cloud-native applications that written by Golang play an important role in the modern computing paradigm. However, existing fuzzers for Golang still employ coarse-grained block coverage metrics, and there is no fuzzer specifically for cloud-native applications, which hinders the bug detection in cloud-native applications. Using fine-grained coverage metrics introduces more seeds and even leads to seed explosion, especially in large targets such as cloud-native applications. Therefore, we employ an accurate edge coverage metric in fuzzer for Golang, which achieves finer test granularity and more accurate coverage information than block coverage metrics. To mitigate the seed explosion problem caused by fine-grained coverage metrics and large target sizes, we propose smart seed selection and adaptive task scheduling algorithms based on a variant of the classical adversarial multi-armed bandit (AMAB) algorithm. Extensive evaluation of our prototype on 16 targets in real-world cloud-native infrastructures shows that our approach detects 233% more bugs than go-fuzz, achieving an average coverage improvement of 100.7%. Our approach effectively mitigates seed explosion by reducing the number of seeds generated by 41% and introduces only 14% performance overhead. [ABSTRACT FROM AUTHOR]

Selection of seed is very crucial for the farmers before the start of the crop season. In this study therefore, an attempt has been made to compare various machine learning (ML) classification techniques for paddy seed forecast for cultivation in three major paddy producing taluk of Madurai district, Tamil Nadu viz Thirumangalam, Peraiyur, and Usilampatti. Five machine learning classification techniques viz. K-nearest neighbour (KNN), decision tree (DT), naive bayes (NB), support vector machine (SVM), and logistic regression (LR) used in this study were compared based on weather data and seed characteristics for the better predictions of a paddy seed. Various measures were used to evaluate the algorithms, including F1-score, accuracy, precision, and recall. The findings indicated that the KNN (K-Nearest Neighbour) gave a better accuracy, precision, recall, and F1-score values of about 0.99, 0.94, 1.0, and 0.96 correspondingly. It gave the best result of the paddy seed selection which may be helpful for the farming community in getting higher yield and profit. [ABSTRACT FROM AUTHOR]

Software vulnerabilities present a significant cybersecurity threat, particularly as software code grows in size and complexity. Traditional vulnerability-mining techniques face challenges in keeping pace with this complexity. Fuzzing, a key automated vulnerability-mining approach, typically focuses on code branch coverage, overlooking syntactic and semantic elements of the code. In this paper, we introduce HotCFuzz, a novel vulnerability-mining model centered on the coverage of hot code blocks. Leveraging vulnerability syntactic features to identify these hot code blocks, we devise a seed selection algorithm based on their coverage and integrate it into the established fuzzing test framework AFL. Experimental results demonstrate that HotCFuzz surpasses AFL, AFLGo, Beacon, and FairFuzz in terms of efficiency and time savings. [ABSTRACT FROM AUTHOR]

Simple Summary: Animal-mediated seed dispersal plays a crucial role in the regeneration of plant populations and the stability of forest ecosystems. In this study, we used the tagging method to measure the effects of seeds of different sizes at different frequencies on seed fate and dispersal distance. We found that rodents consumed more small seeds in situ, dispersed and scatter-hoarded more large seeds, and dispersed large seeds over longer distances. Rodents showed a negative frequency dependence for small seeds and a positive frequency dependence for large seeds on being eaten in situ. Moreover, rodents showed a negative frequency dependence for large seeds and a positive frequency dependence for small seeds on being eaten after removal and scatter-hoarding. These findings provide insights into the frequency dependence of animal predation on seed size, animal-mediated seed dispersal, and the regeneration of plant populations. Frequency-dependent predation is common in predator–prey interactions. Size is an important characteristic of seeds and is crucial in the regeneration stage of plant seeds. However, the frequency dependence of animal predation on seed size has not been reported. In this study, we conducted a field experiment and used different sizes of Liaodong oak (Quercus wutaishanica) seeds to test the frequency dependence of intraspecific seed size selection in rodents. We used the number ratio of large to small seeds as the frequency. The results show that the rate of small seeds being eaten in situ was significantly higher than that of large seeds (p < 0.05). The rates of different-sized seeds being eaten after removal decreased with increasing frequencies, and there was no significant difference between frequencies except for 1:9 and 9:1. The rates of large seeds being scatter-hoarded were significantly higher than those of small seeds at different frequencies (p < 0.05). The eating distances after removal of large seeds were significantly longer than those of small seeds at the same frequencies (p < 0.05). Furthermore, the scatter-hoarding distances of large seeds were significantly longer than those of small seeds at three frequencies (1:9, 3:7, and 9:1) (p < 0.05). That is, rodents consumed more small seeds in situ, dispersed and scatter-hoarded more large seeds, and dispersed large seeds over longer distances. Rodents exhibited a negative frequency dependence for small seeds and a positive frequency dependence for large seeds on being eaten in situ. Moreover, rodents exhibited a negative frequency dependence for large seeds and a positive frequency dependence for small seeds on being eaten after removal and scatter-hoarding. These results reveal the frequency dependence of rodent selection on seed size and provide new insights into animal-mediated seed dispersal and the regeneration of plant populations. [ABSTRACT FROM AUTHOR]

How different stressors impact plant health and memory when they are imposed in different generations in wild ecosystems is still scarce. Here, we address how different environments shape heritable memory for the next generation in seeds and seedlings of Pinus radiata, a long‐lived species with economic interest. The performance of the seedlings belonging to two wild clonal subpopulations (optimal fertirrigation vs. slightly stressful conditions) was tested under heat stress through physiological profiling and comparative time‐series chloroplast proteomics. In addition, we explored the seeds conducting a physiological characterization and targeted transcriptomic profiling in both subpopulations. Our results showed differential responses between them, evidencing a cross‐stress transgenerational memory. Seedlings belonging to the stressed subpopulation retained key proteins related to Photosystem II, chloroplast‐to‐nucleus signalling and osmoprotection which helped to overcome the applied heat stress. The seeds also showed a differential gene expression profile for targeted genes and microRNAs, as well as an increased content of starch and secondary metabolites, molecules which showed potential interest as biomarkers for early selection of primed plants. Thus, these finds not only delve into transgenerational cross‐stress memory in trees, but also provide a new biotechnological tool for forest design. Summary statement: These results provide a deep knowledge of transgenerational cross‐stress priming in long‐lived species, revealing potential biomarkers for early selection of primmed seeds as a core strategy for designing climate‐smart forestry. [ABSTRACT FROM AUTHOR]

With declining funding in agricultural research and development coupled with tremendous pressure from different stakeholders, it is now essential for research institutes to demonstrate the return on investments (RoIs) in projects that they implement. In response to the above need, this study evaluated the RoI of seed sector interventions implemented by the International Potato Center in Malawi with the financial support of 3.425 million euros from Irish Aid, from 2007 to 2017. The project interventions evaluated in this study were seed selection, seed multiplication technologies, and potato (Solanum tuberosum) varieties. The study used the economic surplus model to measure monetary benefits realized by the project, while a cost–benefit analysis was employed to estimate the RoI. Results revealed positive returns on donors' investments over the project's lifespan. The net project worth was estimated at $66.6 million when net benefits are discounted by 6% and $71.8 million when net benefits are discounted by 12% over 23 years project benefit lifespan (2007–2030). The basic RoI found a $6 to $8 return considering a 6% and 12% discount rate, respectively, for every dollar invested. Despite these positive results, further investments are needed in the seed production system for the early seed generations. Effective regulatory interventions on seed quality assurance and continuous awareness creation efforts are required to encourage farmers to use the best seed production methods. Core Ideas: For every dollar invested in the Irish potato seed system in Malawi, there is an $8 in return.There are positive economic returns to Irish Aid investments in the Irish potato seed system in Malawi.Despite positive returns, there is a need for further investments in the early seed generation system. [ABSTRACT FROM AUTHOR]

The Hetian Qing donkey is an excellent local donkey breed in Xinjiang. It is of great significance to accelerate breeding and the speed of breeding and rejuvenation, as well as to understand the genetic basis of the strategies and population. This study collected a total of 4 male donkeys and 28 female donkeys. It then obtained genotype data through Simplified Genomic Sequencing (GBS) technology for data analysis. The results detected a total of 55,399 SNP loci, and the genotype detection rate of individuals was ≥90%. A total of 45,557 SNP loci were identified through quality control, of which 95.5% were polymorphic. The average minimum allele frequency was 0.250. The average observed heterozygosity was 0.347. The average expected heterozygosity was 0.340. The average IBS (state homologous) genetic distance was 0.268. ROH: 49 (homozygous fragments), with 73.47% of the length between 1 and 5 Mb. The average per-strip ROH length was 1.75 Mb. The mean inbreeding coefficient was 0.003. The 32 Hetian green donkeys could be divided into six families. The number of individuals in each family is significant. To sum up, the Hetian Qing donkey population has low heterozygosity, few families, and large differences in the number of individuals in each family, which can easily cause a loss of genetic diversity. In the subsequent process of seed protection, seed selection should be conducted according to the divided pedigree to ensure the long-term protection of the genetic resources of Hetian green donkeys. [ABSTRACT FROM AUTHOR]

This work aimed to propose multivariate selection strategies reconciling the agronomic ideotype and vegetation indices to obtain genotypes with high tillering, early cycle, tolerant to lodging, and foliar diseases. The sowing of white oat lines was carried out on April 27, 2023, allocated in an experimental design of blocks augmented with interspersed controls. The regular treatments correspond to 593 lines of white oat and the common treatments were represented by the cultivars URS Olada, URS Altaneira, and IPR Artêmis arranged in four replications. The measurements of variables of agronomic interest took place in each experimental unit as follows: percentage of tillering, days to flowering, percentage of lodging, percentage of leaf rust, percentage of stem rust, and percentage of leaf spots. The phenomic analyses took place through flights carried out with a specialized drone, equipped with a 20 MP resolution camera and standard height (80 m). The best proposed indices were index for reducing the percentage of lodging with normalized green (NG), green–red ratio (IGR), soil color index (SCI), green leaf index (GLI), visible atmospheric resistant index (VARI), green leaf index 2 (GLI2), red–blue (RB), blue green pigment (BGI), normalized red–blue difference index (NRBDI), green–blue ratio, BIM, normalized green–blue difference index (NGBDI), and ExGR; index for reducing days to flowering with brightness index (BI), NRBDI, days to flowering (DF), gray (GRAY), IGR, average intensity (L), normalized red (NR), RGRI, SCI, BGI, BIM, GLI, NGBDI, and NG; and index for reducing the percentage of stem rust with NG, BI, Gray2, GLI2, RGRI, GLI, VARI, BGI, L, NR, RB, and coloration index. Only two lines were selected concomitantly with multivariate indices, line L167 (M5, SA) and L426 (M6, ETMS), that combine lodging tolerance and leaf rust genes. Core Ideas: New selection strategies for plant breeding with precision and speed.Genotype selection using remote sensing.Multivariate strategy using predicted data.Innovative protocol using phenomics in white oat trials.Agronomic Ideotype combined with indirect selection strategies by unmanned vehicles. [ABSTRACT FROM AUTHOR]

The Influence Maximization problem (IMP) is a fundamental algorithmic challenge that involves selecting a set of k users, known as the seed set, from a social media network to maximize the expected number of influenced users or influence spread. In recent years, Influence maximization has been extensively studied due to its numerous applications in marketing and other domains. However, many existing algorithms neglect the impact of communities on influence maximization, and some other methods suffer from scalability issues and time-consuming computations. In this paper, we propose a fast, and scalable algorithm called Community based Influential maximization (CB-IM) to address these limitations. This method leverages the community structure of the network to select k users, thus maximizing the influence spread. The CB-IM algorithm comprises two main components for influence maximization: (1) seed selection and (2) local community spreading. In the seed selection phase, we extract seed nodes from communities identified through a community detection algorithm. To effectively reduce computational complexity and distribute seed nodes across communities, we carefully select meaningful communities. The second phase involves the propagation of influence within independent communities. In this step, the final seed nodes are strategically distributed to facilitate local spreading through simple paths within the communities. To evaluate the performance of proposed method, we conducted a series of experiments using real networks. The proposed CB-IM algorithm demonstrated superior performance over other algorithms in terms of influence spread and running time, highlighting its effectiveness. [ABSTRACT FROM AUTHOR]

The aim of investigation was to evaluate the pattern of genetic variation in Kabuli chickpea genotypes through various traits under semi-arid rainfed circumstances. Trial was performed to evaluate the response of 50 Kabuli chickpea genotypes via a randomized complete block layout with three replicates. The entry by tester (genotype by trait) biplot which explained 66% of the variability indicated that the important traits for a favorable genotype in semiarid environments were seeds' number of pod and pods' number of single plant. The biplot model introduced some desirable chickpea genotypes as good for a trait or a category of traits; genotype 27 for chlorophyl content, genotype 26 for seed yield (SY), SP and PP, and genotype 36 for plant height (PH), days to maturity (DM), pod's weight (PW), hundred seed weight (HSW), plant dry weight (PDW), and plant fresh weight (PFW). Based on an ideal assumptive genotype (entry) position, genotype 1 followed to 2, 10, 16, 17, 23, 26, 33 and 34 were ideal regarding the distinction ability and typical potential. According to an ideal assumptive trait (tester) position, PH, PDW, and PFW were more discriminative and typical traits. The responses of chickpea genotypes regarding SY indicated that genotype 26 following to 3, 17 and 27, were the most desirable and can be advised for commercial cultivar release process. [ABSTRACT FROM AUTHOR]

China's nascent role in the global agrifood regime manifests itself in varied ways across the world, including in the rising spread of Chinese agricultural inputs in foreign markets. This article examines the character and dynamics of a Chinese seed company in Tajikistan, a country in which China's influence has grown substantially since the early 2010s. Focussing on Tajikistan's politicized cotton sector, I analyse the multiscalar and multiactor processes involved in the promotion of Chinese cotton seed and illuminate that Chinese seed breeders strategically tapped into Chinese state funds for their commercial seed business. However, Tajik actors as well as socio‐economic, technical, and political factors have played a crucial role in mediating the Chinese presence and the commodification of seed. I contend that Tajik farmers' seed selection is not significantly influenced by, what could be called, grand politics. Furthermore, I demonstrate that, while the Chinese state plays a central role in the globalization of seed companies, the materialization of state capital has been shaped by private actors, who operate according to capitalist rationality. [ABSTRACT FROM AUTHOR]

The vulnerabilities in Ethereum virtual machine (EVM) may lead to serious problems for the Ethereum ecosystem. With lots of techniques being developed for the validation of smart contracts, the testing of EVM has not been well‐studied. In this paper, we propose EVMFuzz, the first that uses the differential fuzzing technique to detect vulnerabilities in EVM. The core idea of EVMFuzz is to continuously generate seed contracts for different EVMs' execution, so as to find as many inconsistencies among execution results as possible, and eventually discover vulnerabilities with output cross‐referencing. First, we present the evaluation metric for the internal inconsistency indicator. Then, we construct seed contracts via predefined mutators and employ a dynamic priority scheduling algorithm to guide seed contract selection and maximize the inconsistency. Finally, we leverage different EVMs as cross‐referencing oracles avoiding manual checking. For evaluation, we selected four widely used EVMs for the test, conducted large‐scale mutation on 36,295 real‐world smart contracts, and generated 253,153 smart contracts as initial seeds. Accompanied by manual root cause analysis, we found five previously unknown security bugs and all had been included in the common vulnerabilities and exposures (CVE) database. This paper proposes EVMFuzz, the first that uses the differential fuzzing technique to detect vulnerabilities in Ethereum virtual machine (EVM). EVMFuzz found five previously unknown security bugs in four widely used EVMs, and all had been included in the common vulnerabilities and exposures (CVE) database. [ABSTRACT FROM AUTHOR]

Mobile laser scanning (MLS) systems have become an important technology for collecting and measuring road information for highway maintenance and reconstruction services. However, the efficient and accurate extraction of unstructured road surfaces from MLS point cloud data collected on highways is challenging. Specifically, the complex and unstructured characteristics of road surveying point cloud data lead to traditional 3D point cloud segmentation. When traditional 3D point cloud algorithms extract unstructured road surfaces, over-segmentation and under-segmentation often occur, which affects efficiency and accuracy. To solve these problems, this study introduces an enhanced road extraction method that integrates supervoxel and trajectory information into a traditional region growing algorithm. The method involves two main steps: first, a supervoxel data structure is applied to reconstruct the original MLS point cloud data, which diminishes the calculation time of the point cloud feature vector and accelerates the merging speed of a similar region; second, the trajectory information of the vehicle is used to optimize the seed selection strategy of the regio growing algorithm, which improves the accuracy of road surface extraction. Finally, two typical highway section tests (flat road and slope road) were conducted to validate the positioning performance of the proposed algorithm in an MLS point cloud. The results show that, compared with three kinds of traditional road surface segmentation algorithms, our method achieves an average extraction recall and precision of 99.1% and 96.0%, and by calculating the recall and precision, an F1 score of 97.5% can be obtained to evaluate the performance of the proposed method, for both datasets. Additionally, our method exhibits an average road surface extraction time that is 45.0%, 50.3%, and 55.8% faster than those of the other three automated segmentation algorithms. [ABSTRACT FROM AUTHOR]

Although Chaenomeles is widely used in horticulture, traditional Chinese medicine and landscape greening, insufficient research has hindered its breeding and seed selection. This study investigated the floral phenology, floral organ characteristics, palynology, and breeding systems of Chaenomeles speciosa (Sweet) Nakai. The floral characteristics of C. speciosa were observed both visually and stereoscopically. The microstructures of the flower organs were observed using scanning electron microscopy. Pollen stainability was determined using triphenyl tetrazolium chloride staining. Stigma receptivity was determined using the benzidine-H2O2 method and the post-artificial pollination pollen germination method. The breeding system was assessed based on the outcrossing index and pollen–ovule ratio. The flowers of C. speciosa were bisexual with a flowering period from March to April. The flowering periods of single flowers ranged from 8 to 19 d, and those of single plants lasted 18–20 d. The anthers were cylindrical, with the base attached to the filament, and were split longitudinally to release pollen. The flower had five styles, with a connate base. The ovaries had five carpels and five compartments. The inverted ovules were arranged in two rows on the placental axis. The stigma of C. speciosa was dry and had many papillary protrusions. In the early flowering stage (1–2 d of flowering), the pollen exhibited high stainability (up to 84.24%), but all stainability was lost at 7 d of flowering. Storage at – 20 °C effectively delayed pollen inactivation. The stigma receptivity of C. speciosa lasted for approximately 7 days, and the breeding system was classified as outcrossing with partial self-compatibility. [ABSTRACT FROM AUTHOR]

Introduction: The seeds of wild pea (Pisum) exhibit marked physical dormancy due to impermeability of the seed coat to water, and the loss of this dormancy is thought to have been critical for domestication. Wild pea seed coats are also notably thick and rough, traits that have also reduced during domestication and are anecdotally linked to increased permeability. However, how these traits specifically interact with permeability is unclear. Methods: To investigate this, we examined the genetic control of differences in seed coat characteristics between wild P. sativum ssp. humile and a nondormant domesticated P. s. sativum accession in a recombinant inbred population. QTL effects were confirmed and their locations refined in segregating F4/5 populations. Results: In this population we found a moderate correlation between testa thickness and permeability, and identified loci that affect them independently, suggesting no close functional association. However, the major loci affecting both testa thickness and permeability collocated closely with Mendel's pigmentation locus A, suggesting flavonoid compounds under its control might contribute significantly to both traits. We also show that seed coat roughness is oligogenic in this population, with the major locus independent of both testa thickness and permeability, suggesting selection for smooth seed was unlikely to be due to effects on either of these traits. Discussion: Results indicate loss of seed coat dormancy during domestication was not primarily driven by reduced testa thickness or smooth seededness. The close association between major permeability and thickness QTL and Mendel's 'A' warrant further study, particularly regarding the role of flavonoids. [ABSTRACT FROM AUTHOR]

Coverage-guided fuzzing has been widely applied in software error and security vulnerability detection. The fuzzing technique based on AFL (American Fuzzy Loop) is a common coverage-guided fuzzing method. The code coverage during AFL fuzzing is highly dependent on the quality of the initial seeds. If the selected seeds' quality is poor, the AFL may not be able to detect program paths in a targeted manner, resulting in wasted time and computational resources. To solve the problems that the seed selection strategy in traditional AFL fuzzing cannot quickly and effectively generate high-quality seed sets and the mutated test cases cannot reach deeper paths and trigger security vulnerabilities, this paper proposes an attention mechanism-based generative adversarial network (GAN) seed generation approach for vulnerability mining, which can learn the characteristics and distribution of high-quality test samples during the testing process and generate high-quality seeds for fuzzing. The proposed method improves the GAN by introducing fully connected neural networks to balance the competitive adversarial process between discriminators and generators and incorporating attention mechanisms, greatly improving the quality of generated seeds. Our experimental results show that the seeds generated by the proposed method have significant improvements in coverage, triggering unique crashes and other indicators and improving the efficiency of AFL fuzzing. [ABSTRACT FROM AUTHOR]

The article explores the outcomes and issues surrounding the U.S. Agroecology Summit 2023. While the summit aimed to establish a roadmap for agroecological research and promote its growth globally, it fell short of producing a concrete plan. The article emphasizes the importance of acknowledging the politics of knowledge and valuing diverse perspectives in advancing agroecology. It highlights the need for collaboration, trust-building, and equitable relationships to ensure a just and resilient approach to agroecology. The text also addresses the challenges of engaging with agroecology within academic institutions and calls for ongoing self-reflection, humility, and accountability to transform existing systems and prioritize food sovereignty. [Extracted from the article]

Vaccines are the main pharmaceutical intervention used against the global public health threat posed by influenza viruses. Timely selection of optimal seed viruses with matched antigenicity between vaccine antigen and circulating viruses and with high yield underscore vaccine efficacy and supply, respectively. Current methods for selecting influenza seed vaccines are labor intensive and time-consuming. Here, we report the Machine-learning Assisted Influenza VaccinE Strain Selection framework, MAIVeSS, that enables streamlined selection of naturally circulating, antigenically matched, and high-yield influenza vaccine strains directly from clinical samples by using molecular signatures of antigenicity and yield to support optimal candidate vaccine virus selection. We apply our framework on publicly available sequences to select A(H1N1)pdm09 vaccine candidates and experimentally confirm that these candidates have optimal antigenicity and growth in cells and eggs. Our framework can potentially reduce the optimal vaccine candidate selection time from months to days and thus facilitate timely supply of seasonal vaccines. Vaccines combat global influenza threats, relying on timely selection of optimal seed viruses. Here, authors introduce MAIVeSS, a machine learning assisted framework to streamline vaccine seed virus selection using genomic sequence, expediting seasonal flu vaccine production and supply. [ABSTRACT FROM AUTHOR]

The number of papers on machine learning and deep neural networks applied to intrusion detection systems (IDS) is ever-increasing. Differently from existing work on the topic, this paper explores the effect of training-time randomness of deep neural networks, which is overlooked by the related literature. Training-time randomness is regulated by the seed of the pseudorandom number generator, and affects the performance of IDS models. The seed selection is studied in conjunction with other critical learning parameters: to the best of our knowledge, there are no similar studies in IDS. The experiments are done with a recent and widely consolidated intrusion detection benchmark, which is used to train and test a neural network under different combinations of seeds and parameters both in supervised and semi-supervised learning modes. The results are inferred by a mixture of explorative analysis, design of experiments, and analysis of variance. According to the results, the choice of the seed yields either excellent or scarce detection metrics; more importantly, the seed selection might be as relevant as the other major learning parameters assessed. [ABSTRACT FROM AUTHOR]

Lycopene is an essential antioxidant found in tomatoes (Solanum lycopersicum L.) that can inhibit and prevent cell damage in the body. Thus, enhancing lycopene content is one of the crucial objectives in tomato breeding. Efforts should focus on assembling the tomato with high fruit yield and good quality by conducting transgressive segregant selection through plant breeding programs. The study aimed to select tomato lines with the potential for transgressive segregation based on high fruit yield and lycopene content. The research began in an augmented design combined with factors comprising 54 non-repeated tomato lines, compared with four repeated standard cultivars (Mawar, Chung, Karina, and Tymoti). The tomato lines' planting into seven blocks had no repeats, while the check cultivars for comparison engaged in repeated planting in each block. The observations underwent the analysis of variance (ANOVA), path analysis, correlation, heritability (narrow sense), and transgressive segregants. Based on transgressive segregation, the high-yielding tomato lines selected bore analysis for lycopene content. Based on the plant height and productive bunches per plant (directly supporting the fruit yield), the 14 promising transgressive segregant families' selection comprised 10 MC crosses, MC10.10, MC10.4, MC11.4, MC12.3, MC27.12, MC27.7, MC29.4, MC35.7, MC74.12, and MC8.3 and four KM crosses, KM23.2, KM26.1, KM30.5, and KM.5.5. For increased lycopene content, the five F4 families MC10.10, MC27.12, MC35.7, KM23.2, and KM30.5 showed better performance wherein selecting 10 transgressive segregant lines resulted with high lycopene content and fruit yield. [ABSTRACT FROM AUTHOR]

The community-based influence maximization in social networks refers to the strategy of amplifying the influence by leveraging the underlying connectivity patterns of the online communities. This promotes the widespread dissemination of user-generated content across socially connected entities. In our work, we have combined the simplicity of degree centrality and the bridging strength of betweenness for maximizing the influence performance. We have used community detection for partitioning the whole network into smaller subnetworks for intelligent distribution of seed nodes among these communities for improving the rate of information spreading. The proposed ranking, named as Community Diversified Seed Selection, is compared with degree centrality and betweenness centrality-based ranking in terms of rate of spreading, absolute execution time and algorithmic time complexity. The comparative study was performed on LFR μ = 0.01, LFR μ = 0.02 and LFR μ = 0.03 benchmark networks, and validated using Facebook real-world social network. Our proposed algorithm has better and faster spreading with reasonable time complexity, therefore, making it a suitable choice for larger networks. [ABSTRACT FROM AUTHOR]

[Display omitted] Inferior diffusion capacity and insufficient acid density hinder the practical application of ZSM-48 zeolite. Finding a simple and practical strategy to simultaneously address these two defects remains a challenge. In response to this dilemma, we developed an unconventional seed-assisted synthesis strategy for Al-rich hierarchical ZSM-48 zeolite. This approach allows for achieving a broader range of silica to alumina ratio and accelerates the entire crystallization process through the selection of unconventional seeds. The synergy between the seed and organic template was demonstrated to play a pivotal role in facilitating nucleation. Direct evidence from 1H-29Si CP MAS NMR, TG, and IR results demonstrates that hexamethonium ions (HM2+) electrostatically adsorb at the defect sites on the seed, thereby promoting nucleation sites formation. Smaller seed crystals undergo more etching during the induction period, resulting in additional defects and enhanced nucleation ability. The obtained catalyst exhibits a diffusion time constant (Deff/L2) nine times that of conventional ZSM-48 zeolite when using p -xylene as a probe molecule. In m -xylene isomerization reaction, Al-rich hierarchical ZSM-48 demonstrates excellent stability along with higher selectivity and yield for p -xylene compared to typical ZSM-5 catalysts. Remarkably, long-term testing of 1000 h yields over 22.5 % of p -xylene, indicating the potential of this catalyst as an alternative for xylene isomerization reaction. This work not only advances the practical application process of ZSM-48 catalyst but also provides valuable insights for optimizing other zeolites. [ABSTRACT FROM AUTHOR]

Balanced influence maximization aims to balance the influence maximization of multiple different entities in social networks and avoid the emergence of filter bubbles and echo chambers. Recently, an increasing number of studies have drawn attention to the study of balanced influence maximization in social networks and achieves success to some extent. However, most of them still have two major shortcomings. First, the previous works mainly focus on spreading the influence of multiple target entities to more users, ignoring the potential influence of the correlation between the target entities and other entities on information propagation in real social networks. Second, the existing methods require a large amount of diffusion sampling for influence estimation, making it difficult to apply to large social networks. To this end, we propose a Balanced Influence Maximization framework based on Deep Reinforcement Learning named BIM-DRL, which consists of two core components: an entity correlation evaluation module and a balanced seed node selection module. Specifically, in the entity correlation evaluation module, an entity correlation evaluation model based on the users' historical behavior sequences is proposed, which can accurately evaluate the impact of entity correlation on information propagation. In the balanced seed node selection module, a balanced influence maximization model based on deep reinforcement learning is designed to train the parameters in the objective function, and then a set of seed nodes that maximize the balanced influence is found. Extensive experiments on six real-life network datasets demonstrate the superiority of the BIM-DRL over state-of-the-art methods on the metrics of balanced influence spread and balanced propagation accuracy. [ABSTRACT FROM AUTHOR]

The objective of present study was to assess seed yield and yield related traits of alfalfa clonal progenies. The phenotypic variation of traits within progenies was also determined. Eleven alfalfa clonal progenies of native origin were object of investigation. The experiment was carried out at the Experimental field of the IASS Obraztsov Chiflik, Rousse for three-year period. The traits plant seed yield (PSY), plant height (PH), generative stem number (GSN), inflorescence number (INP), pod number (PNI), seed number (SNP) and 1000-seed weight (TSW) were evaluated. There were statistically significant differences among clonal progenies for all studied traits. Five progenies (PM30, JM13, GM27, SL83 and PM18) were identified as very valuable genetic source with potential for developing high seed yielding varieties. There was established PSY, PNI and SNP exhibited moderate to high phenotypic variability within progenies, while PH and TSW expressed low variability. Data confirm possibility of used the traits number of pods per inflorescence and number of seeds per pod as criteria of germplasm selection for seed yield improvement. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has been applied in many applications. Meanwhile, the quality of DL systems is becoming a big concern. To evaluate the quality of DL systems, a number of DL testing techniques have been proposed. To generate test cases, a set of initial seed inputs are required. Existing testing techniques usually construct seed corpus by randomly selecting inputs from training or test dataset. Till now, there is no study on how initial seed inputs affect the performance of DL testing and how to construct an optimal one. To fill this gap, we conduct the first systematic study to evaluate the impact of seed selection strategies on DL testing. Specifically, considering three popular goals of DL testing (i.e., coverage, failure detection, and robustness), we develop five seed selection strategies, including three based on single-objective optimization (SOO) and two based on multi-objective optimization (MOO). We evaluate these strategies on seven testing tools. Our results demonstrate that the selection of initial seed inputs greatly affects the testing performance. SOO-based selection can construct the best seed corpus that can boost DL testing with respect to the specific testing goal. MOO-based selection strategies can construct seed corpus that achieve balanced improvement on multiple objectives. [ABSTRACT FROM AUTHOR]

This study was conducted to assess and compare the potential seed sources from different regions of Karnataka with the Marayoor seed source. Seeds were collected from seven different locations of Karnataka (S1-Bevinahally; S2-Doranalu; S3-Gottipura; S4-Gungaraghatti; S5-Muddenahally; S6-Narasapura; S7-Tavarekere) and S8-Marayoor seeds were procured from Kerala Forest Research Institute. Significant variations were observed among the seed sources for most of the assessed parameters. Marayoor seed source S8 (68.66%) displayed the highest germination. Among the Karnataka seed sources, Gottipura seed source S3 (51.67%) recorded the higher germination percentage indicating higher probability establishment. Seed source S3 recorded significantly highest growth parameters compared to other Karnataka seed sources throughout the study. The leaf area, leaf area ratio, leaf weight ratio, specific leaf area and specific leaf weight, were assessed and seed source S3 recorded higher results compared to all other Karnataka sources in the study. Apart from Marayoor source, seed source S3 showed the highest Vigour Index (1488.35) as well as the Quality Index (0.249), indicating seedlings with greater initial vigour and overall quality. Seed source S3 also showed the lowest Sturdiness Quotient (13.58), indicating stronger and more stable seedlings. Though B:C ratio of S1-Bevinahally seed source was high, but its nursery performance was low. By keeping nursery performance and B:C ratio in view S3-Gottipura seed source performed best among Karnataka seed sources. These findings can contribute to the selection of suitable seed sources for sandalwood plantation and breeding programs in Karnataka, promoting the conservation and sustainable utilization of this valuable timber species. [ABSTRACT FROM AUTHOR]

'Espresso' (Reg. no. CV‐293, PI 687202) is a lowland ecotype (Gulf sub‐population) of switchgrass (Panicum virgatum L.) developed and released by Mississippi State University and the Mississippi Agricultural and Forestry Experiment Station. Espresso was developed from seven cycles of phenotypic recurrent selection for rapid seed germination without stratification. All cycles of selection were made using seed harvested from open‐pollinated isolation blocks containing approximately 100 mother plants. Germination was assessed in laboratory conditions for 15 years, culminating in 2 years of field‐based germination and emergence evaluation at five locations across Mississippi. Over a 2‐year period, germination and emergence of Espresso was significantly greater (P < 0.05) than seed from 'Alamo' and 'Kanlow'. Field testing revealed Espresso as having significantly (P < 0.05) greater establishment success (mean number of seedlings per linear 30 cm) when compared to Alamo. Espresso was released based on the need for southeastern US‐adapted germplasm that possesses enhanced germination characteristics. Core Ideas: 'Espresso' lowland switchgrass was bred for rapid germination.Phenotypic recurrent selection was used to address/circumvent/eliminate dormancy.'Espresso' outperformed 'Alamo' in field and laboratory experiments. [ABSTRACT FROM AUTHOR]

Summary: The exploration of phenotypic spaces of large sets of plant species has considerably increased our understanding of diversification processes in the plant kingdom. Nevertheless, such advances have predominantly relied on interspecific comparisons that hold several limitations.Here, we grew in the field a unique set of 179 inbred lines of durum wheat, Triticum turgidum spp. durum, characterized by variable degrees of artificial selection. We measured aboveground and belowground traits as well as agronomic traits to explore the functional and agronomic trait spaces and to investigate trait‐to‐agronomic performance relationships.We showed that the wheat functional trait space shared commonalities with global cross‐species spaces previously described, with two main axes of variation: a root foraging axis and a slow–fast trade‐off axis. Moreover, we detected a clear signature of artificial selection on the variation of agronomic traits, unlike functional traits. Interestingly, we identified alternative phenotypic combinations that can optimize crop performance.Our work brings insightful knowledge about the structure of phenotypic spaces of domesticated plants and the maintenance of phenotypic trade‐offs in response to artificial selection, with implications for trade‐off‐free and multi‐criteria selection in plant breeding. [ABSTRACT FROM AUTHOR]

The fast generation of values of the beta random variable is a subject of great interest and multiple applications, ranging from purely mathematical and statistical ones to applications in management and production, among others. There are several methods for generating these values, with one of the essential points for their design being the selection of random seeds. Two interesting aspects converge here: the use of sequences as inputs (and the need for them to verify properties such as randomness and uniformity, which are verified through statistical test suites) and the design of the algorithm for the generation of the variable. In this paper, we analyse, in detail, the algorithms that have been developed in the literature, both from a mathematical/statistical and computational point of view. We also provide empirical development using R software, which is currently in high demand and is one of the main novelties with respect to previous comparisons carried out in FORTRAN. We establish which algorithms are more efficient and in which contexts, depending on the different values of the parameters, allowing the user to determine the best method given the experimental conditions. [ABSTRACT FROM AUTHOR]

Influence Maximization (IM) aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes. However, most existing studies on the IM problem focus on static social network features, while neglecting the features of temporal social networks. To bridge this gap, we focus on node features reflected by their historical interaction behavior in temporal social networks, i.e., interaction attributes and self-similarity, and incorporate them into the influence maximization algorithm and information propagation model. Firstly, we propose a node feature-aware voting algorithm, called ISVoteRank, for seed nodes selection. Specifically, before voting, the algorithm sets the initial voting ability of nodes in a personalized manner by combining their features. During the voting process, voting weights are set based on the interaction strength between nodes, allowing nodes to vote at different extents and subsequently weakening their voting ability accordingly. The process concludes by selecting the top k nodes with the highest voting scores as seeds, avoiding the inefficiency of iterative seed selection in traditional voting-based algorithms. Secondly, we extend the Independent Cascade (IC) model and propose the Dynamic Independent Cascade (DIC) model, which aims to capture the dynamic features in the information propagation process by combining node features. Finally, experiments demonstrate that the ISVoteRank algorithm has been improved in both effectiveness and efficiency compared to baseline methods, and the influence spread through the DIC model is improved compared to the IC model. [ABSTRACT FROM AUTHOR]

To understand how food resource use and partitioning by closely related species allows local coexistence, it is key to determine whether a species' diet reflects food availability or food preferences. Here, we analysed the diets, seed selection, and seed preferences of three closely related harvester ants: Messor barbarus, M. bouvieri, and M. capitatus. Sympatric within a Mediterranean shrubland, these species differ in foraging behaviour and worker polymorphism. For 2 years, we studied the ants' diets and seed selection patterns as well as the local availability of seeds. Additionally, we performed a seed-choice experiment using a paired comparison design, offering the ants seeds from eight native plant species. The three ant species had the same general diet, which was primarily granivorous. Although they all consumed a wide variety of seeds, they mostly selected seeds from a small subset of plant species. Despite their morphological and behavioural differences, the ants displayed similar seed preferences that were highly consistent with their diets and seed selection patterns. Our results support the idea that the trophic ecology of these three harvester ants is driven by similar seed preferences rather than by their morphological and behavioural differences. Seed diversity and abundance were high near the ants' nests, suggesting that seed availability is not limiting and could in fact favour local species coexistence. [ABSTRACT FROM AUTHOR]