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Acacia mearnsii De Wild. has been introduced to over 150 countries for its economic value. However, it easily escapes from plantations and establishes monospecific stands across plains, hills, valleys, and riparian habitats, including protected areas such as national parks and forest reserves. Due to its negative ecological impact, A. mearnsii has been listed among the world’s 100 worst invasive alien species. This species exhibits rapid stem growth in its sapling stage and reaches reproductive maturity early. It produces a large quantity of long-lived seeds, establishing a substantial seed bank. A. mearnsii can grow in different environmental conditions and tolerates various adverse conditions, such as low temperatures and drought. Its invasive populations are unlikely to be seriously damaged by herbivores and pathogens. Additionally, A. mearnsii exhibits allelopathic activity, though its ecological significance remains unclear. These characteristics of A. mearnsii may contribute to its expansion in introduced ranges. The presence of A. mearnsii affects abiotic processes in ecosystems by reducing water availability, increasing the risk of soil erosion and flooding, altering soil chemical composition, and obstructing solar light irradiation. The invasion negatively affects biotic processes as well, reducing the diversity and abundance of native plants and arthropods, including protective species. Eradicating invasive populations of A. mearnsii requires an integrated, long-term management approach based on an understanding of its invasive mechanisms. Early detection of invasive populations and the promotion of public awareness about their impact are also important. More attention must be given to its invasive traits because it easily escapes from cultivation. Full article

To address the issues in existing 3D point cloud facet generation networks, specifically, the tendency to produce a large number of empty facets and the uncertainty in facet count, this paper proposes a novel deep learning framework for robust facet segmentation. Based on the generated facet set, two exploratory applications are further developed. First, to overcome the bottleneck where inaccurate empty-facet detection impairs the downsampling performance, a facet-abstracted downsampling method is introduced. By using a learned facet classifier to filter out and discard empty facets, retaining only non-empty surface facets, and fusing point coordinates and local features within each facet, the method achieves significant compression of point cloud data while preserving essential geometric information. Second, to solve the insufficient precision in organ segmentation within crop point clouds, a facet growth-based segmentation algorithm is designed. The network first predicts the edge scores for the facets to determine the seed facets. The facets are then iteratively expanded according to adjacent-facet similarity until a complete organ region is enclosed, thereby enhancing the accuracy of segmentation across semantic boundaries. Finally, the proposed facet segmentation network is trained and validated using a synthetic dataset. Experiments show that, compared with traditional methods, the proposed approach significantly outperforms both downsampling accuracy and instance segmentation performance. In various crop scenarios, it demonstrates excellent geometric fidelity and semantic consistency, as well as strong generalization ability and practical application potential, providing new ideas for in-depth applications of facet-level features in 3D point cloud analysis. Full article

Direct seeding has gained prominence as a labor-efficient and environmentally sustainable alternative to conventional transplanting in rice cultivation. In direct seeding systems, early-stage management is crucial for stable seedling establishment, with sowing uniformity measured by seed counts being a critical indicator of success. However, conventional manual seed counting methods are time-consuming, prone to human error, and impractical for large-scale or repetitive tasks, necessitating advanced automated solutions. Recent advances in computer vision technologies and precision agriculture tools, offer the potential to automate seed counting tasks. Nevertheless, challenges such as domain discrepancies and limited labeled data restrict robust real-world deployment. To address these issues, we propose a density estimation-based seed counting framework integrating semi-supervised learning and background augmentation. This framework includes a cost-effective data acquisition system enabling diverse domain data collection through indoor background augmentation, combined with semi-supervised learning to utilize augmented data effectively while minimizing labeling costs. The experimental results on field data from unknown domains show that our approach reduces seed counting errors by up to 58.5% compared to conventional methods, highlighting its potential as a scalable and effective solution for agricultural applications in real-world environments. Full article

The present research features an experimental comparative design and the objective of this work was to determine the susceptibility to microbiological contamination in fatty acid methyl esters (FAMEs) and the FAME–water interface of residual and refined lard, large volume simulating storage conditions as fuel supply chain, and to identify the microorganisms developed. The plates were seeded according to ASTM E-1259 and the instructions provided by the manufacturer of the Bushnell Haas agar. Microbiological growth was observed at the FAME–water interface of FAME obtained from residual lard. Using the MALDI-TOF mass spectrometry technique, Pseudomonas aeruginosa and Streptomyces violaceoruber bacteria were identified in the residual lard FAMEs, with the latter being previously reported in FAMEs. The implications of microorganism development on the physicochemical quality of FAMEs are significant, as it leads to an increase in the acid index, which may negatively impact metals by inducing corrosion. The refined lard FAMEs did not show any development of microorganisms. The present research concluded that residual lard tends to be more prone to microbiological attack if the conditions of water and temperature affect microbial growth. The findings will contribute to the knowledge base for a safer introduction of FAMEs into the biofuel matrix. Full article

Accurate and timely estimation of soybean emergence at the plot scale using unmanned aerial vehicle (UAV) remote sensing imagery is essential for germplasm evaluation in breeding programs, where breeders prioritize overall plot-scale emergence rates over subimage-based counts. This study proposes PlotCounter, a deep learning regression model based on the TasselNetV2++ architecture, designed for plot-scale soybean seedling counting. It employs a patch-based training strategy combined with full-plot validation to achieve reliable performance with limited breeding plot data. To incorporate additional agronomic information, PlotCounter is extended into a multitask learning framework (MTL-PlotCounter) that integrates sowing metadata such as variety, number of seeds per hole, and sowing density as auxiliary classification tasks. RGB images of 54 breeding plots were captured in 2023 using a DJI Mavic 2 Pro UAV and processed into an orthomosaic for model development and evaluation, showing effective performance. PlotCounter achieves a root mean square error (RMSE) of 6.98 and a relative RMSE (rRMSE) of 6.93%. The variety-integrated MTL-PlotCounter, V-MTL-PlotCounter, performs the best, with relative reductions of 8.74% in RMSE and 3.03% in rRMSE compared to PlotCounter, and outperforms representative YOLO-based models. Additionally, both PlotCounter and V-MTL-PlotCounter are deployed on a web-based platform, enabling users to upload images via an interactive interface, automatically count seedlings, and analyze plot-scale emergence, powered by a multimodal large language model. This study highlights the potential of integrating UAV remote sensing, agronomic metadata, specialized deep learning models, and multimodal large language models for advanced crop monitoring. Full article

This study investigated the impact of nitrogen (N) fertilization on the yield and grain filling (GF) characteristics of two conventional japonica rice varieties with distinct panicle types: Yangchan 3501 (large-panicle: spikelets per panicle $> 150$) and Nangeng 46 (medium-panicle: $100 <$ spikelets per panicle $< 150$). Field experiments were conducted over two growing seasons (2022–2023) with three N application rates (T1: 225 kg ha⁻¹, T2: 270 kg ha⁻¹, T3: 315 kg ha⁻¹). Key measurements included tiller dynamics, panicle composition, GF parameters modeled using the Richards equation, and enzyme activities related to nitrogen metabolism (Fd-GOGAT, NR) and carbohydrate transport (α-amylase, SPS). Results showed that the yield increased with higher N levels for both varieties, with Yangchan 3501 achieving higher yields primarily through increased grains per panicle (15.65% rise under T3 vs. T1), while Nangeng 46 relied on panicle number (8.83% increase under T3 vs. T1). Nitrogen application enhanced Fd-GOGAT and NR activities, prolonging photosynthesis and improving GF rates, particularly in the inferior grains of Yangchan 3501 during middle and late stages. However, a high N reduced seed-setting rates and 1000-grain weight, with larger panicle types exhibiting a greater sensitivity to N-induced changes in branch structure and assimilate allocation. This study highlights that optimizing N management can improve nitrogen-metabolism enzyme activity and GF efficiency, especially in large-panicle rice, while medium-panicle types require higher N inputs to maximize panicle number. These findings provide actionable insights for achieving high yields and efficient nutrient use in conventional rice cultivation. Full article

Ricinus communis (Euphorbiaceae), commonly known as the castor oil plant, is prized for its versatile applications in medicine, industry, and agriculture. It features large, deeply lobed leaves with vibrant colours, robust stems with anthocyanin pigments, and extensive root systems for nutrient absorption. Its terminal panicle-like inflorescences bear monoecious flowers, and its seeds are enclosed in prickly capsules. Throughout its various parts, R. communis harbours a diverse array of bioactive compounds. Leaves contain tannins, which exhibit astringent and antimicrobial properties, and alkaloids like ricinine, known for anti-inflammatory properties, as well as flavonoids like rutin, offering antioxidant and antibacterial properties. Roots contain ellagitannins, lupeol, and indole-3-acetic acid, known for anti-inflammatory and liver-protective effects. Seeds are renowned for ricin, ricinine, and phenolic compounds crucial for industrial applications such as biodegradable polymers. Pharmacologically, it demonstrates antioxidant effects from flavonoids and tannins, confirmed through minimum inhibitory concentration (MIC) assays for antibacterial activity. It shows potential in managing diabetes via insulin signalling pathways and exhibits anti-inflammatory properties by activating nuclear factor erythroid 2-related factor 2 (Nrf2). Additionally, it has anti-fertility effects and potential anticancer activity against cancer stem cells. This review aims to summarize Ricinus communis’s botanical properties, therapeutic uses, chemical composition, pharmacological effects, and industrial applications. Integrating the current knowledge offers insights into future research directions, emphasizing the plant’s diverse roles in agriculture, medicine, and industry. Full article

The olive oil industry, a key component of Southern Europe’s agricultural sector, generates large amounts of by-products during processing, including olive leaves, branches, stones, and seeds. In the context of growing environmental concerns and limited natural resources—particularly in the Mediterranean regions—there is increasing interest in circular economy approaches that promote the valorization of agricultural residues. These by-products are rich in bioactive compounds, particularly phenolics such as oleuropein and hydroxytyrosol, which are well known for their antioxidant and anti-inflammatory activities. This study aimed to evaluate the phenolic content and antioxidant capacity of by-products from three olive cultivars using high-performance liquid chromatography with photodiode array detection (HPLC–PDA) and mass spectrometry (MS). The leaves and seeds, particularly from the “Cobrança” and a non-identified variety, presented the highest antioxidant activity, as well as the highest concentration of phenolic compounds, demonstrating once again the direct relationship between these two parameters. The identification of the compounds present demonstrated that the leaves and branches have a high diversity of phenolic compounds, particularly secoiridoids, flavonoids, phenylpropanoids, phenylethanoids, and lignans. An inverse relationship was observed between the chlorophyll and carotenoid content and the antioxidant activity, suggesting that phenolic compounds, rather than pigments, are the major contributors to antioxidant properties. Therefore, the by-products of the olive oil industry are a valuable source of sustainable bioactive compounds for distinct industrial sectors, such as the food, nutraceutical, and pharmaceutical industries, aligning with the European strategies for resource efficiency and waste reduction in the agri-food industries. Full article

Camellia vietnamensis grows in a unique tropical environment, and its seed oil has a rich aroma. The content of unsaturated fatty acids in C. vietnamensis oil is up to 90%, which can regulate human lipid metabolism and prevent cardiovascular and cerebrovascular diseases. Compared with olive oil, C. vietnamensis oil has a higher content of unsaturated fatty acids. This study used eleven C. vietnamensis cultivars cultivated on Hainan Island. Among the 11 cultivars, “Boao 1” had fruits with the largest vertical diameter of 45.05 mm, while “Haida 1” had fruits with the largest horizontal diameter, single-fruit weight, and fresh 100-grain weight of 53.5 mm, 70.6 g, and 479.01 g, respectively. “Boao 3” had an acid value and peroxide value of 1.59 mg/g and 3.50 mmol/kg, respectively, and its saponification value content was 213.18 mg/g. “Boao 5” had the highest iodine value, 101.86 g/100 g, among the 11 cultivars. The content of unsaturated fatty acids in the seed oil of 11 cultivars ranged from 84.87% to 87.38%. The qRT-PCR results confirmed that “Boao 3” had a higher content of flavonoids and fatty acids than other cultivars. The comprehensive analysis of physiological and biochemical indices showed that the top five cultivars were “Haida 1”, “Boao 3”, “Haida 2”, “Boao 1”, and “Boao 5”. These five cultivars were suitable for large-scale cultivation in tropical regions, such as Hainan Island. This study provided a theoretical basis for the breeding of C. vietnamensis cultivars in tropical regions. Full article

Compiler optimization is a critical component for improving program performance. However, the Rustc optimization process may introduce vulnerabilities due to algorithmic flaws or issues arising from component interactions. Existing testing methods face several challenges, including high randomness in test cases, inadequate targeting of vulnerability-prone regions, and low-quality initial fuzzing seeds. This paper proposes a test case generation method based on large language models (LLMs), which utilizes prompt templates and optimization algorithms to generate a code relevant to specific optimization passes, especially for real-time control logic and safety-critical modules unique to the industrial control field. A vulnerability screening approach based on static analysis and rule matching is designed to locate potential risk points in the optimization regions of both the MIR and LLVM IR layers, as well as in unsafe code sections. Furthermore, the targeted fuzzing strategy is enhanced by designing seed queues and selection algorithms that consider the correlation between optimization areas. The implemented system, RustOptFuzz, has been evaluated on both custom datasets and real-world programs. Compared with state-of-the-art tools, RustOptFuzz improves vulnerability discovery capabilities by 16%–50% and significantly reduces vulnerability reproduction time, thereby enhancing the overall efficiency of detecting optimization-related vulnerabilities in Rustc, providing key technical support for the reliability of industrial control systems. Full article

Within the framework of organic acid alternatives to chemical herbicides, pre-emergence weed control research is scarce. Citric acid (CA) and lactic acid (LA), considered significantly less effective than pelargonic acid (PA) and acetic acid (AA) from post-emergence (foliar spraying) studies, have largely been disregarded. This in vitro study was aimed at comparing the effects of 5–20% AA, AA + essential oils, PA, CA, and LA on radicle emergence inhibition (direct spraying of seeds) and shoot emergence inhibition (application to peat) on both weeds (perennial ryegrass, green foxtail, common vetch and chicory) and crops (soft wheat, alfalfa and millet). All tested compounds demonstrated concentration-dependent and species-specific effects on shoot emergence inhibition, with CA and LA (IC₅₀ range: 3.4–19.3%) showing a comparable efficacy to PA and AA (IC₅₀ range: 3.1–35.9%). The results also showed that CA and, to a lesser extent, LA were less inhibitory to soft wheat (CA IC₅₀ = 62.5%; LA IC₅₀ = 35.9%) and alfalfa (CA IC₅₀ = 57.8%; LA IC₅₀ = 44.1%) shoot emergence. CA and LA show potential promise for pre-emergence weed control in field testing, either on a stale seedbed in pre-crop sowing or concurrently with soft wheat and alfalfa sowing. Investigating organic compound herbicidal effects on crops of interest warrants attention. Full article

Mauritia flexuosa, commonly known as “canangucha,” holds significant nutritional and economic value in the Amazon region. While its pulp is widely utilized in local food products, the seed or kernel is largely underutilized. This study investigated the proximal and phytochemical composition of M. flexuosa, alongside its biological properties, specifically focusing on the hypoglycemic activity of an ethanolic extract from M. flexuosa seeds (MFSs). Proximal analysis revealed that MFSs are a notable source of crude fiber (28.4%) and a moderate source of protein (9.1%). Phytochemical screening indicated a high total polyphenol content (123.4 mg gallic acid equivalents/100 mg dry weight) and substantial antiradical capacity against the ABTS radical (IC₅₀ = 171.86 µg/mL). Notably, MFS ethanolic extracts exhibited significant in vitro antihyperglycemic activity via inhibiting α-amylase and α-glucosidase enzymes, demonstrating comparable inhibition to acarbose at higher concentrations. This hypoglycemic effect was further corroborated in an in vivo rat model with induced diabetes, where the administration of 100 mg/kg of MFS ethanolic extract significantly reduced blood glucose levels compared to the diabetic control group (p < 0.05). A moderate antihypertensive effect was observed at a concentration of 150 mg/kg, correlating with ACE inhibition. High-performance liquid chromatography–mass spectrometry (UHPLC-ESI-HRMS) analysis of the seed extract identified phenolic compounds including ellagic, p-coumaric, and chlorogenic acids, as well as flavonoids such as quercetin, myricetin, and epicatechin. This study provides the first evidence of the hypoglycemic activity of MFSs, offering valuable insights into their phytochemistry and potential therapeutic applications. Full article

Dodders (Cuscuta spp.; Convolvulaceae) are parasitic weeds that pose major challenges to agriculture due to their ability to infect a wide range of host plants, extract nutrients, and transmit pathogens. Their control is especially challenging because of the seed longevity, resistance to herbicides, and the capacity for vegetative regeneration. Mechanical methods such as hand-pulling or mowing are labour-intensive and often ineffective for large infestations. Chemical control is limited, as systemic herbicides often affect the host species equally, or even worse than the parasite. Current research is exploring biological control methods, including allelopathic compounds, host-specific fungal pathogens, and epiparasitic insects, though these methods remain largely experimental. An integrated approach that combines prevention, targeted mechanical removal, and biological methods offers the most promising path for long-term management. Continued research is essential to develop effective, sustainable control strategies while exploring possible beneficial uses of these complex parasitic plants. The present review aims to thoroughly summarise the existing literature, emphasising the most recent advances and discussing future perspectives. Full article

Efficient and consistent DNA extraction is crucial for genotyping but often hindered by the limitations of traditional manual processes, which are labour-intensive, error-prone, and costly. We introduce a semi-automated, robotic-assisted DNA extraction (RoboCTAB) tailored for large-scale plant genotyping, leveraging advanced yet affordable liquid-handling robotic systems. The protocol/workflow integrates a CTAB extraction protocol specifically adapted for a robotic liquid-handling system, making it compatible with high-throughput genotyping techniques such as SNP genotyping and sequencing. Various plant parts (leaves, roots, manual seed chip) were explored as the source material for DNA extractions, with the aim of identifying the tissue best suited for collection on a large scale. Young roots (radicle) proved the easiest to harvest at scale, while the harvest of leaves and seed chips were more laborious and error-prone. DNA yield and quality from both leaves and roots (but not seed chips) were similar and sufficient for downstream analysis. Interestingly, root tissue could still be extracted from imbibed seeds, even if the seeds failed to germinate, thus proving useful for DNA extraction. Cost analysis indicates significant savings in labour costs, highlighting the approach’s suitability for large-scale projects. Quality assessments demonstrate that the robotic process yields high-quality DNA, maintaining integrity for downstream applications. This semi-automated DNA extraction system represents a scalable, reliable solution for large-scale genotyping that is accessible to many users who cannot implement highly sophisticated and costly systems as are known to exist in large multinational seed companies. RoboCTAB, a low-cost, optimized method for high-throughput DNA extraction, minimizes the risk of cross-contamination. RoboCTAB is capable of processing up to four 96-well plates (384 samples) simultaneously in a single run, improving cost-efficiency and providing seamless integration with laboratory workflows, potentially setting new standards for efficiency and quality in DNA processing and sequencing at scale. Full article

Fuzz testing plays a key role in improving Linux kernel security, but large-scale fuzzing often generates a high number of crash reports, many of which are redundant. These duplicated reports burden triage efforts and delay the identification of truly impactful bugs. Syzkaller, a widely used kernel fuzzer, clusters crashes using instruction pointers and sanitizer metadata. However, this heuristic may misgroup distinct issues or split similar ones caused by the same root cause. To address this, we present ECHO, a lightweight call stack-based deduplication tool that analyzes structural similarity among kernel stack traces. By computing the longest common subsequence (LCS) between normalized call stacks, ECHO groups semantically related crashes and improves post-fuzzing analysis. We integrate ECHO into the Syzkaller fuzzing workflow and use it to prioritize inputs that trigger deeper, previously untested kernel paths. Evaluated across multiple Linux kernel versions, ECHO improves average code coverage by 15.2% and discovers 20 previously unknown bugs, all reported to the Linux kernel community. Our results highlight that stack-aware crash grouping not only streamlines triage, but also enhances fuzzing efficiency by guiding seed selection toward unexplored execution paths. Full article