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Ground filtering is crucial for airborne Light Detection and Ranging (LiDAR) data post-processing. The progressive triangulated irregular network densification (PTD) algorithm and its variants outperform others in accuracy, stability, and robustness, using grid-based seed point selection, TIN construction, and iterative rules for ground point identification. However, these methods still face limitations in removing low points and accurately preserving terrain details, primarily due to their sensitivity to grid size. To overcome this issue, a novel PTD filtering algorithm based on an adaptive grid (AGPTD) was proposed. The main contributions of the proposed method include an outlier removal method using a radius outlier removal algorithm and Kd-tree, a method for establishing an adaptive two-level grid based on point cloud density and terrain slope, and an adaptive selection method for angle and distance thresholds in the iterative densification processing. The performance of the AGPTD algorithm was assessed based on widely used benchmark datasets. Results show that the AGPTD algorithm outperforms the classical PTD algorithm in retaining ground feature points, especially in reducing Type I error and average total error significantly. In comparison with other advanced algorithms developed in recent years, the novel algorithm showed the lowest average Type I error, the minimal average total error, and the greatest average Kappa coefficient, which were 1.11%, 2.28%, and 90.86%, respectively. Additionally, the average accuracy, precision, and recall of AGPTD were 97.69%, 97.52%, and 98.98%, respectively. [ABSTRACT FROM AUTHOR]

Backcrossing between Sikitita and its male parent Arbequina, offers the possibility to check the suitability of different self-incompatibility models proposed for olive. To determine Sikitita's response to self- and cross-pollination treatments, including pollination with its father Arbequina, we compared the parameters following pollen–pistil interaction, the resulting initial and final fruit set, and the paternity of the seeds produced under different crosses. The results showed that Sikitita behaves as a self-incompatible cultivar due to the inhibition of pollen tube growth in the pistil of self-pollinated flowers. This incompatibility reaction led to a significant reduction of self-fertilization and fruit set. Seed paternity analyses confirmed the self-incompatibility response of Sikitita. A similar incompatibility response was observed in Sikitita flowers when hand-pollinated with pollen of Arbequina and Koroneiki. On the contrary, cross-pollination with Arbosana gave excellent results, with analyses showing that pollen of Arbosana is largely preferred by Sikitita to father its seeds more than the pollen of other cultivars presented in the orchard. The backcross failure of Sikitita with Arbequina pollen suggests that the self-incompatibility system in olives is not of the gametophytic type. In contrast, pollination tests fit features of previously reported sporophytic self-incompatibility systems. However, some amendments are proposed, among them the incompatibility groups for Sikitita and Koroneiki. [ABSTRACT FROM AUTHOR]

Sexual selection is not a single process. Instead, multiple processes of sexual selection can interact with respect to a given phenotype, in either reinforcing, independent, or conflicting directions. Here we consider how different processes of sexual selection interact in the seed bug Lygaeus simulans. This species is characterised by limited pre‐copulatory sexual selection, but the potential for rather strong post‐copulatory sexual selection. In particular, mating failure is common in this species, with around 40%–60% of copulations failing to result in the successful transfer of sperm. Mating failure is negatively correlated with female size, with smaller females being less likely to end up inseminated. We have recently argued that this pattern is best explained by cryptic male mate choice for large, more fecund females. Males therefore preferentially inseminate larger females. Here we explore how this potential cryptic male choice interacts with another component of post‐copulatory sexual selection: sperm competition. We first manipulated male and female size variation, generating large and small, male and female, size classes. Using a visible mutant marker to assign paternity, we then double‐mated females with males, in all combinations of male and female size. Our results showed that sperm competition outcomes were primarily driven by copulation duration, with longer copulations leading to greater paternity share for a male. We also confirmed that larger females are more likely to produce offspring than smaller females, as predicted by cryptic male choice for large females. This effect was again linked to copulation duration, with longer copulations less likely to lead to mating failure. While larger males tended to be more successful in sperm competition, especially if copulating second, female size had little effect on paternity, suggesting that cryptic male choice and sperm competition are acting relatively independently in this species. [ABSTRACT FROM AUTHOR]

In order to investigate vulnerabilities in stateful protocols, AFL-NET has put forward stateful protocol fuzz testing. In such fuzz testing, the selection of seeds makes a major contribution to the exploration of paths. However, current stateful protocol fuzz testers often repeatedly execute the same several seeds, resulting in an inability to effectively explore more paths. To alleviate this problem, starting from the gain of seeds, this paper proposed an effective seed dynamic scheduling algorithm based on stateful protocols. The algorithm utilized the potential gain, actual gain, and cost of seeds as the gain, using this gain to dynamically schedule seeds and allocate the number of times seeds. Experiments show that this method significantly improves the number of vulnerabilities found and also has a certain degree of improvement in increasing coverage, indicating that the definition of this gain and the seed scheduling algorithm can effectively select seeds and explore more paths and vulnerabilities. [ABSTRACT FROM AUTHOR]

Climate change is motivating a reassessment of how seeds are selected for reforestation, as rapid environmental change can lead to local maladaptation in trees. Genetic association studies and past seed source climate both have the potential to help identify appropriate planting stock, but these techniques have not been compared and tested as part of an operational planting program. In this study, we combined an analysis of single nucleotide polymorphisms (SNPs) associated with environmental gradients in sugar pine (Pinus lambertiana) with an analysis of post‐fire seedling survival and growth in a restoration experiment. Our genotype–environment association (GEA) tests of 92 individuals from varying climates within CA revealed 829 SNPs (out of 300,604) with significant association with climate gradients, especially April snowpack. Of these, 323 either had annotations that suggested potential functional importance or were identified by two different methods. We then built Bayesian models of survival and growth for all seedlings in a separate post‐fire planting experiment, to test the relative predictive ability of source elevation (a common proxy for source climate) versus the proportion of seedling alleles expected to be locally advantageous based on GEA. Across three sites within the King Fire scar in Eldorado National Forest in 2017, 2018, and 2019, 1774 seedlings were planted. Of these, 206 had enough green needles in 2020 to allow sample collection, and 161 were successfully genotyped. We found that source elevation was generally better at predicting seedling performance than genotype indices, perhaps because of the limited scope of the association analysis. Seed sources from 500 to 1800 ft (152.4–548.6 m) lower in elevation, and one seed zone further south generally performed as well or better than local seed sources. This result, and those of similar previous studies, suggest that "climate matching" using past climate information for existing seed sourcing units is a reasonable starting point for finding seedlings suited to already‐altered planting site climate conditions. However, further tests with more extensive genomic and performance data may improve the utility of genotype information for seed selection. [ABSTRACT FROM AUTHOR]

Summary Climate change is disrupting floral traits that mediate mutualistic and antagonistic species interactions. Plastic responses of these traits to multiple shifting conditions may be adaptive, depending on natural selection in new environments. We manipulated snowmelt date over three seasons (3–11 d earlier) in factorial combination with growing‐season precipitation (normal, halved, or doubled) to measure plastic responses of volatile emissions and other floral traits in Ipomopsis aggregata. We quantified how precipitation and early snowmelt affected selection on traits by seed predators and pollinators. Within years, floral emissions did not respond to precipitation treatments but shifted with snowmelt treatment depending on the year. Across 3 yr, emissions correlated with both precipitation and snowmelt date. These effects were driven by changes in soil moisture. Selection on several traits changed with earlier snowmelt or reduced precipitation, in some cases driven by predispersal seed predation. Floral trait plasticity was not generally adaptive. Floral volatile emissions shifted in the face of two effects of climate change, and the new environments modulated selection imposed by interacting species. The complexity of the responses underscores the need for more studies of how climate change will affect floral volatiles and other floral traits. [ABSTRACT FROM AUTHOR]

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]

Background: Auxiliary diagnosis of different types of cystic lung diseases (CLDs) is important in the clinic and is instrumental in facilitating early and specific treatments. Current clinical methods heavily depend on accumulated experience, restricting their applicability in regions with less developed medical resources. Thus, how to realize the computer‐aided diagnosis of CLDs is of great clinical value. Purpose: This work proposed a deep learning‐based method for automatically segmenting the lung parenchyma in computed tomography (CT) slice images and accurately diagnosing the CLDs using CT scans. Methods: A two‐stage deep learning method was proposed for the automatic classification of normal cases and five different CLDs using CT scans. Lung parenchyma segmentation is the foundation of CT image analysis and auxiliary diagnosis. To meet the requirements of different sizes of the lung parenchyma, an adaptive region‐growing and improved U‐Net model was employed for mask acquisition and automatic segmentation. The former was achieved by a self‐designed adaptive seed point selection method based on similarity measurement, and the latter introduced multiscale input and multichannel output into the original U‐Net model and effectively achieved the lightweight design by adjusting the structure and parameters. After that, the middle 30 consecutive CT slice images of each sample were segmented to obtain lung parenchyma, which was employed for training and testing the proposed multichannel parallel input recursive MLP‐Mixer network (MPIRMNet) model, achieving the computer‐aided diagnosis of CLDs. Results: A total of 4718 and 16 290 CT slice images collected from 543 patients were employed to validate the proposed segmentation and classification methods, respectively. Experimental results showed that the improved U‐Net model can accurately segment the lung parenchyma in CT slice images, with the Dice, precision, volumetric overlap error, and relative volume difference of 0.96 ± 0.01, 0.93 ± 0.04, 0.05 ± 0.02, and 0.05 ± 0.03, respectively. Meanwhile, the proposed MPIRMNet model achieved appreciable classification effect for normal cases and different CLDs, with the accuracy, sensitivity, specificity, and F1 score of 0.8823 ± 0.0324, 0.8897 ± 0.0325, 0.9746 ± 0.0078, and 0.8831 ± 0.0334, respectively. Compared with classical machine learning and convolutional neural networks‐based methods for this task, the proposed classification method had a preferable performance, with a significant improvement of accuracy of 10.74%. Conclusions: The work introduced a two‐stage deep learning method, which can achieve the segmentation of lung parenchyma and the classification of CLDs. Compared to previous diagnostic tasks targeting single CLD, this work can achieve various CLDs' diagnosis in the early stage, thereby achieving targeted treatment and increasing the potential and value of clinical applications. [ABSTRACT FROM AUTHOR]

Organism condition plays an important role in sexual selection. Sexual ornaments and displays can be condition‐dependent, reflecting either underlying genetic quality, experience of environmental stressors, or both. As such, the phenotypic expression of such traits, and the resulting patterns of mate choice acting on them, may be shaped by intrinsic genetic quality and the environment. Moreover, condition may also influence the choosing individual in mate choice, influencing their ability to invest in mate discrimination, or changing what traits of the chosen, including resources, are most preferred. Here we consider sexual selection and condition in the seed bug Lygaeus simulans, a species characterised by strong post‐copulatory sexual selection, but rather limited pre‐copulatory discrimination. We manipulated short‐term condition in both males and females by restricting access to water for 24 h. Water is particularly important in these bugs, given their feeding ecology and physiology. We found that water‐deprived males proved less likely to mate, while copulation duration with water‐deprived females was significantly reduced. Given the importance of copulation duration for the successful transfer of sperm by males to females, the data suggest cryptic male choice acting against water‐deprived females. These data add to those suggesting that cryptic male choice for fecund females plays an important role in sexual selection in this species. More generally, our results support the widespread importance of condition in terms of mating dynamics and sexual selection. [ABSTRACT FROM AUTHOR]

Japanese walnuts (Juglans mandshurica) are known to be seed‐dispersed by the hoarding of two species of rodents, Japanese squirrels (Sciurus lis) and large Japanese field mice (Apodemus speciosus). The previous study showed that the squirrel transported and stored larger walnut seeds farther, whereas the mice transported smaller seeds farther. Divergent selection by different seed dispersers may play an important role in differentiation between local walnut populations, in which understanding seed size heritability is crucial. This study aims to determine how the seed size selected by rodents affects the initial growth of walnuts and to what extent walnut seed size is genetically determined. Sixty walnut seeds were collected from a small seed‐producing tree, and 50 walnut seeds from a large seed‐producing tree were sown and grown for 18 years. The seed size produced from each tree showed that the heritability of seed size was high (h2 = 0.52). When 50 smaller and 50 larger walnut seeds were planted under the same conditions, the dry weight of shoots and roots of the larger seeds was significantly greater than that of the smaller seeds, and root length was also significantly longer in the larger seeds. Seed size is heritable, so if squirrels are inhabiting, larger seeds, which are advantageous for regeneration, will be selected, and walnut trees producing larger seeds will be passed on to the next generation. Because selective hoarding has implications for seed establishment success, inhabiting a suitable seed disperser, the squirrel, is more critical for walnut regeneration than previously assumed. [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]

Ornamental plant breeding enables the selection of cultivars with desired features from numerous genotypes; however, this process is time-consuming and resource-demanding. Aiming to establish a pre-selection protocol that can facilitate the selection of dwarf rose varieties, the connection between anatomical and histological characteristics and the vegetative growth of rose cultivars was examined. To assess the adaptive potential of the studied cultivars, intra-annual cambial dynamics were explored relative to the observed meteorological fluctuations during the growing season. The investigation included six garden rose cultivars from the 'Reka' and 'Pixie' collections, bred under semi-arid open-field conditions in Serbia. Plant height ranged from 20 to 68 cm, with differing growth habits and types. Vegetative growth was significantly correlated with the xylem/phloem ratio, the proportion of total vessel area relative to cross-sectional and xylem areas, vessel-related features, and porosity (correlation coefficients up to 0.78). Regeneration via cambial activity and the formation of false rings were observed in five of the six cultivars studied, with meteorological analysis suggesting that precipitation and temperature triggered cambial reactivation. This approach effectively targets key parameters in the selection of dwarf and climate-resilient rose cultivars, facilitating the development of reliable pre-selection criteria. [ABSTRACT FROM AUTHOR]

Seed scheduling is a critical step of greybox fuzzing, which assigns different weights to seed test cases during seed selection, and significantly impacts the efficiency of fuzzing. Existing seed scheduling strategies rely on manually designed models to estimate the potentials of seeds and determine their weights, which fails to capture the rich information of a seed and its execution and thus the estimation of seeds' potentials is not optimal. In this article, we introduce a new seed scheduling solution, Graphuzz, for coverage-guided greybox fuzzing, which utilizes deep learning models to estimate the potentials of seeds and works in a data-driven way. Specifically, we propose an extended control flow graph called e-CFG to represent the control-flow and data-flow features of a seed's execution, which is suitable for graph neural networks (GNN) to process and estimate seeds' potential. We evaluate each seed's code coverage increment and use it as the label to train the GNN model. Further, we propose a self-attention mechanism to enhance the GNN model so that it can capture overlooked features. We have implemented a prototype of Graphuzz based on the baseline fuzzer AFLplusplus. The evaluation results show that our model can estimate the potential of seeds and has the robust capability to generalize to different targets. Furthermore, the evaluation using 12 benchmarks from FuzzBench shows that Graphuzz outperforms AFLplusplus and the state-of-the-art seed scheduling solution K-Scheduler and other coverage-guided fuzzers in terms of code coverage, and the evaluation using 8 benchmarks from Magma shows that Graphuzz outperforms the baseline fuzzer AFLplusplus and SOTA solutions in terms of bug detection. [ABSTRACT FROM AUTHOR]

The First International Virtual Exchange between African and New Zealand Researchers IVEANZR hosted by the University of Canterbury (NZ) was held via Zoom over 2 h on 15 May 2024. There was no registration fee for the conference. The purpose of the virtual conference was to provide a forum for postgraduate students and researchers to present their latest research in an international conference environment but without the burden of a registration fee and the prohibitive cost of international travel, particularly for many emerging researchers. There were 148 registered participants from several countries in the conference and nine presentations were made successfully. The successful event supports the notion that a conference held virtually is a sustainable, low carbon emission, interactive way to share knowledge across international borders. [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]

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]

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]

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.)

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]

Cotton is a resilient and multipurpose crop, meeting major of the world’s textile needs while also yielding byproducts like edible oil and animal feed. Starch plays a crucial role in cotton fabric production. It enhances fabric strength by forming a protective film around cotton fibers, making them more resistant to wear and tear. BES1 (brassinosteroid insensitive 1) is a key regulator in brassinosteroid signaling. It controls thousands of target genes involved in development processes. Interestingly, two β-amylase proteins (BAM7 and BAM8) are part of the BES1 family, despite their primary function as β-amylases. β-Amylase (BAM) and BES1 are two gene families with functional and regulatory roles in controlling shoot growth and development by mediating brassinosteroid effects. They share similar domains and participate in various biological processes, tolerance and responses to stresses like salt and drought. In a computational analysis comparing Arabidopsis and Gossypium species, BAM and BES1 were characterized. BES1 genes were grouped into four clusters based on the comparison of the two species. Two clusters corresponded to BAM7 and BAM8, while the other two clusters were associated with BES1. The conserved nucleotide domain sequence is GCTGGATGG. Short tandem repeats include TG and TTG, which can serve as molecular markers. BES1 is specifically linked to cellulose and fiber production and holds promise as a candidate for plant selection and breeding in Gossypium (cotton). [ABSTRACT FROM AUTHOR]

Being one of the most abundant genera of orchids, Dendrobium presents a valuable genetic resource for hybridization programs. Morphological characterization and assessment of genetic parameters plays a crucial role in establishing genetic connections among orchid species within the same genus. The study aims to discern the morphological traits of five Indonesian Dendrobium species, intending to evaluate their potential as candidates for crossbreeding programs. The materials used in this study were D. antennatum, D. discolor, D. stratiotes, D. lineale, D. gouldii, and D. sylvanum. This research examines 21 quantitative traits and 21 qualitative morphological traits of the studied Dendrobium. The findings reveal variations in characters related to flowers, leaves, and pseudobulbs. The analysis of genetic parameters indicates the presence of genetic diversity in traits such as flower stalk length, length of inflorescence, flower series length, flower length, flower width, dorsal sepal width, lateral sepal width, petal length, petal width, labellum length, labellum width, and the number of florets. All observed traits demonstrate high heritability. So, the characters that have high genetic variability and heritability are valuable in selection criteria for plant breeding. [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]

Copyright of Brazilian Journal of Production Engineering / Revista Brasileira de Engenharia de Produção is the property of Brazilian Journal of Production Engineering 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.)

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]

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]

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 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]

Indonesia is the largest supplier of patchouli oil in the world market, contributing 80%-90%. Most patchouli oil products are exported in the perfume, cosmetics, pharmaceutical, antiseptic, aromatherapy, and insecticide industries. The emergence of patchouli leaf disease significantly reduced the production of wet, dry, oil, and patchouli alcohol. Therefore, selecting patchouli cuttings (seedlings) that are entirely healthy and disease-free is very important to prevent disease transmission from one area to another. In addition, the selection of disease-free seeds is also essential to prevent the use of diseased patchouli plant propagation. So far, the early identification of patchouli plant health is carried out through visual observations by experts using antiviral serum tested in the laboratory. However, this testing process is expensive. Therefore, in this paper, we proposed a novel Convolutional Neural Network (CNN) architecture for patchouli leaf diseases. We proposed a system for early identification of whether a patchouli leaf is diseased or healthy. Our CNN model uses three convolution layers, a dense layer, and a dropout layer. We compare the proposed model with well-known models, namely EfficientNetB0, AlexNet, InceptionV3, MobileNetV2, and VGG16. The results show that the proposed model outperformed five well-known models as a comparison. It has been confirmed by predicting the new and different testing data. This research contributes to the early identification of patchouli leaf diseases to reduce the expensive costs of identifying patchouli leaf diseases. [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]