Extraction Method Research Articles

Unlocking the potential of Allium dictyoprasum C.A. Meyer ex Kunth: quantum chemical insights into radical scavenging, chemical composition, phenolic content, and antimicrobial activity

ABSTRACT Allium dictyoprasum C.A. Meyer ex Kunth (A. dictyoprasum) underwent comprehensive analysis, encompassing quantum chemical computations to assess its radical scavenging potential, chemical and elemental composition, total phenolic content, and antimicrobial activity. Experimental and theoretical investigations focused on elucidating the radical scavenging properties of polyhydroxy phenolic compounds present in the plant. Quantum chemical calculations were employed to evaluate the antioxidants employed to evaluate selected polyhydroxy phenolic molecules including flavonoids, hydrocinnamic acid derivatives, and hydroxybenzoic acid derivatives from natural sources. Thermochemical parameters of these compounds were calculated by the B3LYP/6–311 G++(d,p) level in both gas and solvent phases to elucidate the radical scavenging mechanism including hydrogen atom transfer (HAT), single electron transfer followed by proton transfer (SET-PT) and sequential proton loss electron transfer (SPLET). Analysis of A. dictyoprasum extracts obtained via various extraction methods revealed the presence of several major compounds, including dimethyl trisulfide, 3,5-Dihydroxy-6-methyl-2,3-dihydro-4 H-pyran-4-one, 2-Methoxy-4-vinylphenol, Dimethyl phthalate, Methyl palmitate, Methyl oleate, Methyl stearate, (9Z)-9-Octadecenamide. Notably, Malic acid and Quinic acid were identified as major compounds, with concentrations of 43.31 and 17.47 mg kg−1 extract, respectively, based on LC-MS/MS analysis. The total phenolic content of the extract was measured as 17.83 mg gallic acid/mL, while its free radical scavenging activity was 80.89% per mg/mL. Elemental analysis revealed significant levels of Mg, K, Na, Fe, and P, with minor concentrations of elements such as Ti, Tl, B, and Be. Furthermore, A. dictyoprasum exhibited notable antibacterial activity against various bacteria strains, surpassing the efficacy of some commercial antibiotics.

Antimicrobial, phytochemical, and antioxidant characterization of the leaf extracts of Clematis montana and Clematis grata

The worldwide emergence of multidrug resistant bacterial infections among the population has impaired the existing effectiveness of antimicrobial treatments, necessitating the hunt for other alternatives. We aimed to find the antibacterial and antioxidant activities of Clematis montana and C. grata leaf extracts (both species have medicinal use for the local population). Extracts were assessed against four pathogenic bacterial strains (Escherichia coli, Salmonella typhi, Pseudomonas aeruginosa, and Staphylococcus aureus), through the agar well diffusion method. Total flavonoid contents (TFC), total phenolic contents (TPC), and 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity were quantified. Results suggested that all plant extracts, particularly those extracted with methanol, were potentially active in suppressing pathogenic bacterial growth (particularly against P. aeruginosa and E. coli) although their efficiency varied when compared to vancomycin. However, the antibacterial activity of methanolic extracts from both species was not greatly influenced by the method of extraction (maceration/soxhlet apparatus). TFC was significantly higher in the methanolic extracts of both species in comparison to the n-hexane extracts, whereas TPC and DPPH scavenging activity were detected to be highest in the methanolic extract from the leaves of C. grata. Our results suggested that the leaf extracts from both Clematis species, particularly the methanolic-based extract of C. montana, might serve as a promising source of an effective antibacterial agent.

Plant-based therapies for urolithiasis: a systematic review of clinical and preclinical studies.

Urolithiasis, the formation of kidney stones, is a common and severe condition. Despite advances in understanding its pathophysiology, affordable treatment options are needed worldwide. Hence, the interest is in herbal medicines as alternative or supplementary therapy for urinary stone disease. This review explores the use of plant extracts and phytochemicals in preventing and treating urolithiasis. Following PRISMA standards, we systematically reviewed the literature on PubMed/Medline, focusing on herbal items evaluated in in vivo models, in vitro studies, and clinical trials related to nephrolithiasis/urolithiasis. We searched English language publications from January 2021 to December 2023. Studies assessing plant extracts and phytochemicals' therapeutic potential in urolithiasis were included. Data extracted included study design, stone type, plant type, part of plant used, solvent type, main findings, and study references. A total of 64 studies were included. Most studies used ethylene glycol to induce hyperoxaluria and nephrolithiasis in rat models. Various extraction methods were used to extract bioactive compounds from different plant parts. Several plants and phytochemicals, including Alhagi maurorum, Aerva lanata, Dolichos biflorus, Cucumis melo, and quercetin, demonstrated potential effectiveness in reducing stone formation, size, and number. Natural substances offer an alternative or supplementary approach to current treatments, potentially reducing pain and improving the quality of life for urolithiasis patients. However, further research is needed to clarify their mechanisms of action and optimize their therapeutic use. The potential of plant-based therapies in treating urolithiasis is promising, and ongoing research is expected to lead to treatment advancements benefiting patients globally.

Extraction optimization and reactivity of 7α-acetoxy-6β-hydroxyroyleanone and ability of its derivatives to modulate PKC isoforms

Protein kinase C is a family of kinases that play important roles in carcinogenesis. Medicinal plants from Plectranthus spp. (Lamiaceae) are a well-known source of interesting abietanes, such as 7α-acetoxy-6β-hydroxyroyleanone (Roy). This study aimed to extract and isolate Roy from P. grandidentatus Gürke, comparing two extraction methods (CO2 supercritical and ultrasound-assisted acetonic extraction), and design new royleanone derivatives for PKC modulation focusing on breast cancer therapy. The concentration of Roy in the extracts was determined by HPLC–DAD. The supercritical extraction method yielded 3.6% w/w, with the presence of 42.7 μg mg−1 of Roy (yield of 0.13%), while ultrasound-assisted acetonic extraction yielded 2.3% w/w, with the presence of 55.2 μg mg−1 of Roy (yield of 0.15%). The reactivity of Roy was investigated aiming at synthetizing new ester derivatives through standard benzoylation and esterification reactions. The benzoylated (Roy-12-Bz) and acetylated (Roy-12-Ac) derivatives in the C12 position were consistently prepared with overall good yields (33–86%). These results indicate the 12-OH position as the most reactive for esterification, affording derivatives under mild conditions. The reported di-benzoylated (RoyBz) and di-acetylated (RoyAc) derivatives were also synthesized after increasing the temperature (50 °C), reaction time, and using an excess of reagents. The cytotoxic potential of Roy and its derivatives was assessed against breast cancer cell lines, with RoyBz emerging as the most promising compound. Derivatization at position C-12 did not offer advantages over di-esterification at positions C-12 and C-6 or over the parent compound Roy and the presence of aromatic groups favored cytotoxicity. Evaluation of royleanones as PKC-α, βI, δ, ε, and ζ activators revealed DeRoy’s efficacy across all isoforms, while RoyPr showed promising activation of PKC-δ but not PKC-ζ, highlighting the influence of slight structural changes on isoform selectivity. Molecular docking analysis emphasized the importance of microenvironmental factors in isoform specificity, underscoring the complexity of PKC modulation and the need for further exploration.

Valorisation of citrus peel industrial wastes for facile extraction of extractives, pectin, and cellulose nanocrystals through ultrasonication: An in-depth investigation

In this work we developed an eco-friendly valorisation of Citrus wastes (CWs), through a solvent-assisted ultrasonication extraction technique, thus having access to a wide range of bio-active compounds and polysaccharides, extremely useful in different industrial sectors (food, cosmetics, nutraceutical). Water-based low-amplitude ultrasonication was examined as a potential method for pectin extraction as well as polar and non-polar citrus extractives (CEs), among which hesperidin and triglycerides of 18 carbon fatty acids were found to be the most representative ones. In addition, citric acid:glycerol (1:4)-based deep eutectic solvent (DES) in combination with ultrasonic extraction was utilized to extract microcellulose (CMC), from which stable cellulose nanocrystals (CNCs) with glycerol-assisted high amplitude ultrasonication were obtained. The physical and chemical properties of the extracted polysaccharides (pectin, micro and nanocellulose) were analysed through DLS, ζ-potential, XRD, HP-SEC, SEM, AFM, TGA-DSC, FTIR, NMR, and PMP-HPLC analyses. The putative structure of the extracted citrus pectin (CP) was analysed and elucidated through enzyme-assisted hydrolysis in correlation with ESI-MS and monosaccharide composition. The developed extraction methods are expected to influence the industrial process for the valorisation of CWs and implement the circular bio-economy.

An adaptive chatter detection method for micro milling based on variational mode extraction

Current chatter detection methods using variational mode decomposition (VMD) hardly select appropriate intrinsic mode functions (IMFs) for characterizing chatter in micro milling. This article introduces a novel variational mode extraction (VME) method to isolate a single IMF representing chatter. An adaptive filter removes chatter-independent components, enabling VME to identify the desired IMF with adaptive calculations for center frequency and penalty factor. The Hilbert-Huang transform is then applied to obtain the time-frequency distribution of the IMF, and various features are computed. Finally, two methods are employed: streaming feature selection considers feature interaction, and radial basis function support vector machine selects pertinent features to establish the chatter detection model. Extensive micro milling tests validate the method, achieving an average detection accuracy of 99.25%.

A standardized chamuangone enriched extract from Garcinia cowa Roxb. leaves shows acute and sub-acute safety

Ethnopharmacological relevanceThe safety assessment of herbal products is critical for their appropriate pharmacological applications. Garcinia cowa Roxb., commonly known as Cha-muang in Thai, has ethnopharmacological relevance in inflammation, infectious diseases, and diabetes. The leaf extracts of G. cowa have been extensively reported for their anticancer, anti-inflammatory, antimicrobial and antioxidative effects. Notably, chamuangone is their major active constituent that contributes to various pharmacological properties. Aim of the studyThe current study aims to establish a standardized chamuangone enriched extract (CEE) and assess its acute and sub-acute toxicities in animal models. MethodologyCEE was established from G. cowa leaves using microwave-assisted extraction (MAE), followed by fractionation and enrichment through silica gel vacuum and column chromatography. The concentration of chamuangone in the leaves was quantified using validated quantitative high-performance liquid chromatography (HPLC) method. The safety profile of CEE was thoroughly evaluated in rodents according to the Organization for Economic Cooperation and Development (OECD) guidelines, specifically 425 and 407. The effect on oxidative stress markers such as superoxide dismutase (SOD), reduced glutathione (GSH), catalase (CAT), and malondialdehyde (MDA) levels were also evaluated in various organs. ResultsBase on the quantitative HPLC analysis the CEE containing 73.0 ± 2% w/w of chamuangone in the extract. In the acute toxicity study, following up and down procedure the female rats were dosed with CEE at 1750 and 550 mg/kg body weight (b.w.), with CEE 1750 mg/kg b.w. identified as toxic, causing mortality, while CEE 550 mg/kg b.w. was deemed safe. The LD50 value was calculated according to the standard protocols, resulting in 970 mg/kg b.w. In histopathological examination, CEE 550 mg/kg b.w. were safe in all the selected organs while the CEE 1750 mg/kg b.w. treated rats exhibited toxic effects in histological tissues sections in the form of necrosis in brain, cardiac muscle hypertrophy, liver inflammation, mild untoward in the spleen, fibrosis in lungs, pancreatitis, pyelonephritis, and ovaries cyst. Administration of CEE at doses of 550 mg/kg b.w. (single dose) in acute and 100 mg/kg b.w. (regularly 28-days) in sub-acute toxicities studies significantly (p < 0.05) decreased levels of uric acid, triglycerides, and cholesterol. Importantly, the CEE 550 and 100 mg/kg b.w. also significantly increased the levels of antioxidant enzymes (SOD, GSH and CAT) and decreased malondialdehyde levels. Normal histopathology was observed in the sub-acute toxicity study in all treated groups. ConclusionThe study successfully concludes that CEE at a dose of 100 mg/kg b.w. is safe for therapeutic application or use as a chemo preventive functional food utilizing green extraction methods. However, chronic toxicity studies are recommended to validate safety concerns over an extended period.

APPN: An Attention-based Pseudo-label Propagation Network for few-shot learning with noisy labels

Few-shot learning has garnered significant attention in deep learning as an effective approach for addressing the issue of data scarcity. Conventionally, training datasets in few-shot learning are clean. However, due to the occurrence of sensor malfunctions, data transmission anomalies, or inaccuracies in manual annotation, the accuracy of sample annotations cannot be guaranteed. Therefore, dealing with few-shot learning with noisy label becomes an urgent problem. To solve the above problem, we propose an Attention-based Pseudo-label Propagation Network (APPN). We make some technical contributions: (1) We propose an attention-based feature extraction method that can effectively capture the distinctions between clean and noisy samples. (2) We propose an improved graph-based pseudo-label propagation method that utilizes pseudo-labels with latent class information as initial labels, thereby enhancing the accuracy of label propagation. (3) We describe a comprehensive multi-step noise detection method which can accurately detect noisy samples from clean samples and effectively distinguish between out-of-domain (OOD) noise and in-domain (ID) noise. (4) Finally, the solid experiments verify the better performance on FC100, CIFAR-FS, miniImageNet, and tieredImageNet datasets. The results show that our method has high robustness and superiority in the few-shot learning with noisy labels. Code and models at: https://github.com/Typistchen/APPN.

A comparative study of extraction methods for recovery of bioactive components from brown algae Sargassum serratifolium

A comparative study of extraction methods for recovery of bioactive components from brown algae Sargassum serratifolium

Natural gas pipeline leak diagnosis based on manifold learning

Natural gas pipeline leakage is a common safety hazard, which can have a great impact on the economy and the environment. This paper proposed a novel manifold learning-enabled feature extraction method for natural gas pipeline leakage diagnosis. Firstly, the natural gas pipeline working condition signal is decomposed and denoised by Variational mode decomposition (VMD). Secondly, the denoised pipeline signals were constructed into a form expressed by the Symmetric positive definite matrix (SPD) using the VMD reconstruction technique, and the geodesic distance measurement method was applied to the SPD matrix to make the data located on the SPD manifold. Then feature extraction is carried out by Local linear embedding (LLE) method based on asymmetric distance. Finally, pattern recognition of the features extracted in this paper by Support vector machine (SVM) can achieve 100% recognition accuracy. By enabling faster and more accurate leak detection, the method minimizes gas loss, as well as mitigating the environmental risks caused by this potent greenhouse gas.

Reducing Washout of Proteins from Defatted Soybean Flakes by Alkaline Extraction: Fractioning and Characterization

Human health, sustainable development, numerous environmental issues, and animal welfare are increasingly driving research and development of plant-based protein products that can serve as meat substitutes. This trend is expected to continue in the coming years due to growing consumer awareness, with people gradually shifting from animal-based foods to more sustainable plant-based options. Soy proteins are a valuable source of plant proteins and are widely used in human and animal diets due to their nutritional value and health benefits. In this study, soybean protein extraction by two methods was compared: water extraction (lower salt content) and Tris-HCl extraction (higher salt content), aiming to characterize the resulting protein fractions. These fractions were studied using differential precipitation based on the isoelectric point. Protein identification by SDS-PAGE, scanning electron microscopy (SEM) for cellular structure assessment, and Fourier-transform infrared spectroscopy (FTIR) were used to determine residual protein left in the solid fraction after extraction using the two methods. Electrophoresis assays revealed the presence of the four main protein fractions (2S, 7S, 11S, and soy whey proteins) in the defatted soybean flakes, establishing the protein profile of Brazilian soybeans and for the two main waste streams of the production process—spent flakes and whey. The separation of fractions was carried out by differential precipitation. FTIR analysis indicated higher residual protein levels in solid residues after the water extraction method compared to the Tris-HCl extraction method. SEM analysis revealed the removal of protein bodies in both extraction methods and the presence of residual oil-containing bodies. Both methodologies are viable alternatives for the industrial separation of soybean protein fractions. Differential precipitation could be implemented to produce isolated products and improve the nutritional profile, increase process yield thus generating less industrial waste and driving the process towards environmental sustainability.

Stabilization of lead (Pb)- and arsenic (As)-contaminated soil using pen shells (Atrina pectinata).

Pen shells (PS), a type of shellfish, are abundantly consumed, and their inedible shell residues are often discarded near the coast without consideration of reutilization. This study sought to investigate the use of natural pen shells (NPS) and calcined pen shells (CPS) to stabilize Pb and As-contaminated soil. During the investigation, NPS and CPS were applied to the contaminated soil in amounts ranging from 1 to 10 wt% and cured for 28days. After the curing process, the mineral phase was examined through X-ray powder diffraction (XRD) and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX) analysis. The XRD and SEM-EDX results revealed the presence of riversideite and ettringite, which contribute to Pb and As stabilization in the CPS-treated soil. The leachability of Pb and As in the treated soil was further examined with three types of chemical extraction methods. Extraction results using 0.1M HCl displayed a notable pH fluctuation in the extractant due to the residual amendments (NPS and CPS). The fluctuation resulted in a strong correlation of leached Pb and As with the pH of the extractant, which might hinder an accurate assessment of stabilization. In order to minimize the effect of pH, an EDTA-NH4OAc extraction was employed, suggesting its potential as a suitable assessment method. EDTA-NH4OAc extraction showed a higher effectiveness of CPS than NPS at 10 wt% of input amounts. In the SBET extraction, that uses a strongly acidic solution, a higher As leachability was observed by increasing the addition of CPS, which implied a CPS-related chemical fixation mechanism. The comparison of various extraction methods showed a higher CPS effectiveness as compared to NPS. However, it was recommended that CPS-treated soil required caution in strongly acidic conditions, especially for arsenic. This study explores the applicability of PS, which has not been investigated as an amendment for Pb and As-contaminated soil previously. Furthermore, this study revealed that utilization of various extraction methods is beneficial for gaining a comprehensive understanding of the role of CaCO3-based amendment in Pb and As-contaminated soil.

Syringin: a naturally occurring compound with medicinal properties

Syringin, a phenylpropanoid glycoside, is widely distributed in various plants, such as Acanthopanax senticosus (Rupr. et Maxim.) Harms, Syringa reticulata (BL) Hara var. mandshurica (Maxim.) Hara, and Ilex rotunda Thumb. It serves as the main ingredient in numerous listed medicines, health products, and foods with immunomodulatory, anti-tumor, antihyperglycemic, and antihyperlipidemic effects. This review aims to systematically summarize syringin, including its physicochemical properties, plant sources, extraction and separation methods, total synthesis approaches, pharmacological activities, drug safety profiles, and preparations and applications. It will also cover the pharmacokinetics of syringin, followed by suggestions for future application prospects. The information on syringin was obtained from internationally recognized scientific databases through the Internet (PubMed, CNKI, Google Scholar, Baidu Scholar, Web of Science, Medline Plus, ACS Elsevier, and Flora of China) and libraries. Syringin, extraction and separation, pharmacological activities, preparations and applications, and pharmacokinetics were chosen as the keywords. According to statistics, syringin can be found in 23 families more than 60 genera, and over 100 species of plants. As a key component in many Chinese herbal medicines, syringin holds significant research value due to its unique sinapyl alcohol structure. Its diverse pharmacological effects include immunomodulatory activity, tumor suppression, hypoglycemic action, and hypolipidemic effects. Additionally, it has been shown to provide neuroprotection, liver protection, radiation protection, cardioprotection, and bone protection. Related preparations such as Aidi injection, compound cantharidin capsule, and Tanreqing injection have been widely used in clinical settings. Other studies on syringin such as extraction and isolation, total synthesis, safety profile assessment, and pharmacokinetics have also made progress. It is crucial for medical research to deeply explore its mechanism of action, especially regarding immunity and tumor therapy. Meanwhile, more robust support is needed to improve the utilization of plant resources and to develop extraction means adapted to the needs of industrial biochemistry to further promote economic development while protecting people’s health.

An adaptive feature mode decomposition-guided phase space feature extraction method for rolling bearing fault diagnosis

Abstract The extraction of fault features from rolling bearings is a challenging and highly important task. Since they have complex operating conditions and are usually under a strong noise background. In this study, a novel approach termed phase space feature extraction guided by an adaptive feature mode decomposition (AFMDPSFE) is proposed to detect subtle faults in rolling bearings. Initially, a new method using Kullback-Leiber divergence is introduced to automatically select the optimal mode number and filter length for the decomposition of vibration signals, facilitating the automatic extraction of optimal components and ensuring efficient screening. This eliminates the need for manual configuration of feature mode decomposition parameters. Furthermore, a criterion that could determine two crucial parameters to capture system dynamics characteristics in phase space reconstruction is embedded into AFMDPSFE algorithm. Subsequently, a series of high-dimensional independent components is derived. The envelope spectrum of the principal component exhibiting the highest kurtosis value is computed to achieve fault identification, consequently enhancing the separation of signal from noise. Both simulations and experimental results confirm the effectiveness of AFMDPSFE approach. A comparison analysis shows the excellent performance of AFMDPSFE in extracting fault features from significant noise interference.&amp;#xD;

Pressed Solid Target Production of 89Zr and its Application for Antibody Labelling.

Zirconium-89 ( 89Zr, t1/2=3.27d) is an important + emitting radionuclide used in Positron Emission Tomography (PET) immuno studies due to its unique characteristics and increased demand due to simple and cost-effective production capacity. Production of 89Zr is achieved primarily through solid natural yttrium targets via different target preparation methodologies, such as electrodeposition, pressed foils, and spark plasma sintering. In this study, we have investigated the pressed solid target methodology. The Yttrium Oxide (Y2O3) powder was pressed to pellet form and stacked over a different back support plate, such as platinum (Pt), niobium (Nb), and tantalum (Ta). The target was irradiated with approximately 12 MeV proton beam for 10-60 minutes at 20µA current. The irradiated target was purified through a solid phase extraction method via hydroxamate-based resin by manual or automatic approach. The purified 89Zr was analyzed using gamma scintigraphy, and specific activity was calculated through Deferoxamine (DFO) chelation. 89Zr radionuclide via pressed target was effectively produced with a production yield of 20-30 MBq/µA.h, and the purification was achieved in 35 minutes with (87.46)% average recovery and >98% purity while using automated purification, but manual purification took 2 hours with (91 ± 2)% recovery and >98% purity. The production yield was comparable to the reported pressed target approach. Deferoxamine (DFO) chelation with 89Zr-oxalate was performed with purity >98% and specific activity of 25-30 µCi/mmol. In this study, we explored the production of 89Zr by pressed targets and purification via manual or automated methods with good radionuclide purity. The chelation with DFO or its analog was performed with good labeling efficiency and stability</p>.

Dissipation and dietary risk assessment of fluoxapiprolin (and its metabolites) residues in cucumber and tomato samples under field conditions

The present study was undertaken to understand the dissipation behaviour/kinetics of fluoxapiprolin and its metabolites in cucumber and tomato under field conditions. A QuEChERS based extraction method followed by liquid chromatography coupled to mass spectrometry (LC-MS/MS) analysis showed that all method validation parameters were within the acceptable range as per international standards with a limit of quantitation (LOQ) of 0.01 mg kg−1 for all analytes. As significant matrix effects were observed with a few metabolites, matrix matched standards were used for the whole study. Residues of fluoxapiprolin in cucumber at standard dose were steady from 0 to 3 day after application and were below LOQ on the 5th day after application. In cucumber fruit at double dose and in tomato at both the doses the residues followed second-order kinetics and were respectively ≤ LOQ from days 7 and 14 onwards. Pre-harvest intervals (PHI) of 5 days and 14 days are proposed for cucumber and tomato fruits respectively. All the metabolites were ≤ LOQ from day 0 in all the matrices. The consumer risk, assessed as Hazard Quotient (HQ), showed that HQ was ≤1 in all the cases. The results of the present study and earlier studies on other similar fungicides suggest that the use of fluoxapiprolin in cucumber and tomato fruits may not pose health or environmental hazards provided that good agricultural practices are followed and the proposed waiting period is observed. The data from the present study can be used by regulatory bodies in establishing maximum residue limits.

A methodological framework proposal for managing risk in small-scale farming through the integration of knowledge and data analytics

IntroductionClimate change and weather variability pose significant challenges to small-scale crop production systems, increasing the frequency and intensity of extreme weather events. In this context, data modeling becomes a crucial tool for risk management and promotes producer resilience during losses caused by adverse weather events, particularly within agricultural insurance. However, data modeling requires access to available data representing production system conditions and external risk factors. One of the main problems in the agricultural sector, especially in small-scale farming, is data scarcity, which acts as a barrier to effectively addressing these issues. Data scarcity limits understanding the local-level impacts of climate change and the design of adaptation or mitigation strategies to manage adverse events, directly impacting production system productivity. Integrating knowledge into data modeling is a proposed strategy to address the issue of data scarcity. However, despite different mechanisms for knowledge representation, a methodological framework to integrate knowledge into data modeling is lacking.MethodsThis paper proposes developing a methodological framework (MF) to guide the characterization, extraction, representation, and integration of knowledge into data modeling, supporting the application of data solutions for small farmers. The development of the MF encompasses three phases. The first phase involves identifying the information underlying the MF. To achieve this, elements such as the type of knowledge managed in agriculture, data structure types, knowledge extraction methods, and knowledge representation methods were identified using the systematic review framework proposed by Kitchemhan, considering their limitations and the tools employed. In the second phase of MF construction, the gathered information was utilized to design the process modeling of the MF using the Business Process Model and Notation (BPMN).Finally, in the third phase of MF development, an evaluation was conducted using the expert weighting method.ResultsAs a result, it was possible to theoretically verify that the proposed MF facilitates the integration of knowledge into data models. The MF serves as a foundation for establishing adaptation and mitigation strategies against adverse events stemming from climate variability and change in small-scale production systems, especially under conditions of data scarcity.DiscussionThe developed MF provides a structured approach to managing data scarcity in small-scale farming by effectively integrating knowledge into data modeling processes. This integration enhances the capacity to design and implement robust adaptation and mitigation strategies, thereby improving the resilience and productivity of small-scale crop production systems in the face of climate variability and change. Future research could focus on the practical application of this MF and its impact on small-scale farming practices, further validating its effectiveness and scalability.

Open-Pit Mining Area Extraction Using Multispectral Remote Sensing Images: A Deep Learning Extraction Method Based on Transformer

Open-Pit Mining Area Extraction Using Multispectral Remote Sensing Images: A Deep Learning Extraction Method Based on Transformer

RIRNet: A Direction-Guided Post-Processing Network for Road Information Reasoning

Road extraction from high-resolution remote sensing images (HRSIs) is one of the tasks in image analysis. Deep convolutional neural networks have become the primary method for road extraction due to their powerful feature representation capability. However, roads are often obscured by vegetation, buildings, and shadows in HRSIs, resulting in incomplete and discontinuous road extraction results. To address this issue, we propose a lightweight post-processing network called RIRNet in this study, which include an information inference module and a road direction inference task branch. The information inference module can infer spatial information relationships between different rows or columns of feature images from different directions, effectively inferring and repairing road fractures. The road direction inference task branch performs the road direction prediction task, which can constrain and promote the road extraction task, thereby indirectly enhancing the inference ability of the post-processing model and realizing the optimization of the initial road extraction results. Experimental results demonstrate that the RIRNet model can achieve an excellent post-processing effect, which is manifested in the effective repair of broken road segments, as well as the handling of errors such as omission, misclassification, and noise, proving the effectiveness and generalization of the model in post-processing optimization.

Extracting Geoscientific Dataset Names from the Literature Based on the Hierarchical Temporal Memory Model

Extracting geoscientific dataset names from the literature is crucial for building a literature–data association network, which can help readers access the data quickly through the Internet. However, the existing named-entity extraction methods have low accuracy in extracting geoscientific dataset names from unstructured text because geoscientific dataset names are a complex combination of multiple elements, such as geospatial coverage, temporal coverage, scale or resolution, theme content, and version. This paper proposes a new method based on the hierarchical temporal memory (HTM) model, a brain-inspired neural network with superior performance in high-level cognitive tasks, to accurately extract geoscientific dataset names from unstructured text. First, a word-encoding method based on the Unicode values of characters for the HTM model was proposed. Then, over 12,000 dataset names were collected from geoscience data-sharing websites and encoded into binary vectors to train the HTM model. We conceived a new classifier scheme for the HTM model that decodes the predictive vector for the encoder of the next word so that the similarity of the encoders of the predictive next word and the real next word can be computed. If the similarity is greater than a specified threshold, the real next word can be regarded as part of the name, and a successive word set forms the full geoscientific dataset name. We used the trained HTM model to extract geoscientific dataset names from 100 papers. Our method achieved an F1-score of 0.727, outperforming the GPT-4- and Claude-3-based few-shot learning (FSL) method, with F1-scores of 0.698 and 0.72, respectively.