Extraction Method Research Articles

Nutritional quality of fish oil extracted from selected freshwater fish species

There is increased consumer awareness of the nutritional importance of fish oil consumption in recent time, however there is paucity of information on the quality of oil obtained from freshwater fishes available in this study area. Nutritional quality of fish oil extracted from 9kg each of Chrysichthys nigrodigitatus, Synodontis membranaceous and Clarias gariepinus were investigated with the aim of providing baseline information on the nutritional quality of oil in the fish available around the study area. Wet rendering, soxhlet extraction and microwave assisted extraction methods were used. Data collected were subjected to appropriate statistical analysis using SPSS 6.0. version. S. membranaceous have the largest oil yield while C. nigrodigitatus have the lowest oil yield; wet rendering oil extraction method gave the highest oil yield. The physical quality characteristics of S. membranaceous was the best with the lowest melting point (28.73 ±0.39°C), specific gravity (0.11± 0.01) and refractive index (1.14 ±0.01) while C. gariepinus oil had the poorest physical quality characteristics. Oil of C. nigrodigitatus have better chemical qualities than the rest having the lowest saponification value (9.81±0.01 mg KOH/g), iodine value (1.59±0.01 gl/100g), acid value (1.12±0.01 mgKOH/g) and free fatty acid (0.56±0.01%) while C. gariepinus oil was the poorest. C. nigrodigitatus also have the highest polyunsaturated fatty acid (PUFA) (18.75), highest Ʃn-3 FA (23.45), highest DHA (Docosahexaenoic acid) (4.93), highest eicosapentaenoic acid EPA+DHA (7.8). The oils chemical qualities are in the order of C. nigrodigitatus> S. membranaceous> C. gariepinus and are of good quality. Oil obtained from C. nigrodigitatus is therefore recommended due to its good chemical qualities using wet rendering oil extraction method. Information provided in this study will guide industrialists and nutritionists on the best extraction method and fish that will give highest oil yield and best oil quality in the study area.

Hybrid model for extractive single document summarization: utilizing BERTopic and BERT model

Extractive text summarization has been a popular research area for many years. The goal of this task is to generate a compact and coherent summary of a given document, preserving the most important information. However, current extractive summarization methods still face several challenges such as semantic drift, repetition, redundancy, and lack of coherence. A novel approach is presented in this paper to improve the performance of an extractive summarization model based on bidirectional encoder representations from transformers (BERT) by incorporating topic modeling using the BERTopic model. Our method first utilizes BERTopic to identify the dominant topics in a document and then employs a BERT-based deep neural network to extract the most salient sentences related to those topics. Our experiments on the cable news network (CNN)/daily mail dataset demonstrate that our proposed method outperforms state-of-the-art BERT-based extractive summarization models in terms of recall-oriented understudy for gisting evaluation (ROUGE) scores, which resulted in an increase of 32.53% of ROUGE-1, 47.55% of ROUGE-2, and 16.63% of ROUGE-L when compared to baseline BERT-based extractive summarization models. This paper contributes to the field of extractive text summarization, highlights the potential of topic modeling in improving summarization results, and provides a new direction for future research.

A survey on planet leaf disease identification and classification by various machine-learning technique

An overview of methods for identifying plants diseases is given in this article. Each sample is categorized by being divided into various groups. The approach of classification involves identifying healthy and diseased leaves based on morphological traits including texture, color, shape, and pattern, among others. Sorting and categorizing plants can be challenging, especially when doing so across a large area, due to the closeness of their visual qualities. There are several methods based on computer vision and image processing. Selecting the right categorization method can be difficult because the outcomes rely on the data you supply. There are several applications for the categorization of plant leaf diseases in fields like agriculture and biological research. This article gives a summary of several approaches currently in use for identifying and categorizing leaf diseases, as well as their benefits and drawbacks. These approaches include preprocessing methods, feature extraction and selection methods, datasets employed, classifiers, and performance metrics

A modified QuEChERS-based UPLC-MS/MS method for rapid determination of multiple antibiotics and sedative residues in freshwater fish

Antibiotics and sedatives are used in freshwater fish culture and transportation, and residue in freshwater fish pose potential risks to human health. Therefore, a throughput method was developed to detect antibiotic and sedative residues in fish, simultaneously quantifying 68 antibiotics and 9 sedatives in freshwater fish using a modified QuEChERS extraction method and UPLC-MS/MS. Matrix-matched calibrations demonstrated good correlation coefficients (R2 > 0.995), with a recovery range of 66.2–118.5%. The intra-day and inter-day relative standard deviation (RSD) were below 9.7% and 12.8%, respectively. The limits of detection (LOD) and quantification (LOQ) were 0.08–1.46 μg/kg and 0.25–4.86 μg/kg, respectively. 68.8% of analytes had weak matrix effects, and 13.0% had moderate matrix effects. In addition, diazepam and many types of antibiotics were detected in30 freshwater fish. The validation parameters were in agreement with the acceptable criteria of the Codex guidelines. The method was effective in analyzing antibiotic and sedative residues in freshwater fish.

Resource-efficient federated learning over IoAT for rice leaf disease classification

Rice is an important staple food in Asia. It is produced and consumed in large quantities. It contributes to 15% of protein intake and 21% of total per capita energy intake in the region, underscoring its important role as a primary global food source. Conversely, rice plants are heavily affected by bacterial, fungal and other microbial diseases, resulting in reduced plant health and crop yields and posing a major challenge for rice farmers. Manual diagnosis of these diseases is particularly problematic in regions with a shortage of agricultural experts. Farmers with insufficient experience sometimes incorrectly identify these diseases by hand. Recent advances in deep learning (DL) models offer a promising solution through automatic image recognition systems that can be very helpful in accurately identifying these diseases. This manuscript presents a resource-efficient federated learning IoAT (Internet of Agriculture Things) approach for rice leaf disease classification. This approach incorporates two key strategies, namely federated transfer learning and feature extraction, and evaluates their performance on various metrics such as accuracy, loss, precision, recall, AUC, and resource-related parameters such as GPU memory consumption, virtual memory, CPU and GPU process utilization. The dataset used in the study includes 5932 images of rice leaf diseases categorized as bacterial leaf blight, blast, brown spot and tungro. The research investigates the application of federated transfer learning and federated feature extraction techniques to classify rice leaf disease images. It performs a comparative analysis of their performance and resource utilization. EfficientNetB3, with an impressive validation accuracy of 99%, is identified as the base model for the federated learning (FL) environment. Furthermore, we implement FL using both transfer learning and feature extraction methods and compare their results on a number of performance and resource related parameters. It is shown that the federated feature extraction approach has lower GPU memory and process utilization compared to the federated transfer learning approach and is thus more resource efficient. Extracting features before model training results in a lightweight model, which is especially beneficial for FL, IoAT devices that require efficient execution on edge devices. Therefore, the proposed approach is presented as a lightweight, privacy-friendly and resource-efficient solution for rice leaf disease classification.

Enhancing deep eutectic solvent systems for efficient fermentable sugar recovery from lignocellulosic fiber

Efficient conversion of sugars into fermentable sugars is a critical challenge in the cost-effective production of lignocellulosic biopolymers and biofuels. This study focuses on various sugar quantification techniques applied to Furcraea Foetida (Mauritius Hemp) samples, utilizing natural deep eutectic solvents (NADES) and deep eutectic solvents (DES) like urea, glycerol, citrates, pyrogallol (PY), and cetyltrimethylammonium bromide (CTAB). Employing a Taguchi-designed experiment, operational conditions were fine-tuned to evaluate the influence of time, concentration, and temperature on each deep eutectic solvent-based process. The emerging green solvent extraction approach demonstrated significant results, achieving notably high sugar yields compared to traditional techniques such as alkali, hot-water, and acid-mediated extraction. At a CTAB:PY molar ratio of 1:3, optimized for 60 min at 50 °C, the highest fermentable sugar (FS) yield of 0.6891 ± 0.0123 g FS/g LCB was attained—2 to 6 times higher than non-optimized values and 0.2 to 0.3 times higher than optimized traditional methods. In light of this, this research study emphasizes the pivotal significance of efficient sugar conversion through optimized deep eutectic solvent-based extraction methods, with a particular focus on Furcraea Foetida fibers, offering promising outcomes for the biofuel and biopolymer production industry.

Performance Analysis of Deep Learning and Machine Learning Methods for Music Genre Classification System

Performance Analysis of Deep Learning and Machine Learning Methods for Music Genre Classification System

Comparative analysis of explainable AI models for predicting lung cancer using diverse datasets

Lung cancer prediction is crucial for early detec-tion and treatment, and explainable AI models have gained attention for their interpretability. This study aims to compare various explainable AI models using diverse datasets for lung cancer prediction. Clinical, genomic, and imaging data from multiple sources were collected, prepro-cessed, and used to train models such as Logistic Regression, SVC-Linear, SVC-rbf, Decision Tree, Random Forest, AdaBoost Classifier, and XGBoost Classifier. Preliminary results indicate that Random Forest achieved the highest accuracy of 98.9% across multiple datasets. Evaluation metrics such as accuracy, precision, recall, and F1 score were utilized, along with interpretability techniques like feature importance rankings and rule extraction methods. The study's findings will aid in identifying effective and interpretable AI models, facilitating early detection and treatment decisions for lung cancer.

Successful synthesis of eco-friendly Metal-Organic framework ([Ni(BDC)]n) allows efficient extraction of multiresidues pesticides and dyes from fish samples

Contaminants such as pesticides and dyes pose substantial challenges to environmental and human health, especially when found in aquatic ecosystems and ingested through the food chain. This requires the development of methods that are efficient, safe and environmentally friendly. In this scenario, this study presents the synthesis and characterization of a metal–organic framework (MOF), specifically [Ni(BDC)]n, designed as a solid adsorbent for the determination of pesticides, including atrazine, bifenthrin, chlorpyrifos, endrin, heptachlor, flutriafol and permethrin, as well as dyes such as crystal violet (CV) and Congo red (CR) in fish samples. Using Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FT-IR), Thermogravimetric Analysis (TGA), X-ray Diffraction (XRD), and Brunauer-Emmett-Teller (BET), we verified the success of the MOF synthesis. BET analysis indicated MOF surface area and total pore volume of 2700 m2/g and 0.0136 cm3 g−1, respectively. The adsorption study revealed high affinity of the MOF for the target analytes, with qmax = 208.33 mg g−1 for pesticides and qmax = 4.18 mg g−1 for dyes. The kinetic study demonstrated that the adsorption mechanism follows a pseudo-second order model for all analytes. Furthermore, employing [Ni(BDC)]n, we have developed an efficient extraction method using Matrix-Solid Phase Dispersion (MSPD) combined with Gas Chromatography-Mass Spectrometry (GC–MS) and UV/Vis spectrophotometry for determination of pesticide and dyes, respectively, with pre-concentration factor ranging from 10 to 100 times for the studied analytes. The methods showed satisfactory linearity for both contaminants (pesticides and dyes), with recovery efficiency ranging from 81-103 % and precision < 13 % (n = 3) for the analytes. Linear ranges covering 10.0–500.0 μg L−1 (R2 > 0.995) for pesticides and 100.0–1000.0 μg L−1 (R2 > 0.988) for dyes. The limits of detection (LOD) and quantification (LOQ) ranged from 0.5-1.6 μg L−1 and 1.7–5.6 μg L−1 for pesticides, respectively, while for dyes, LOD and LOQ ranged from 2.0-12.0 μg L−1 and 7.6–39.6 μg L−1, respectively. These results highlight the potential of [Ni(BDC)]n as a promising material for efficient and selective extraction in the sustainable determination of pesticides and dyes in fish samples, with satisfactory reuse.

Next-Gen Linguistic Intelligence Image Summarizer for Documents Using Attention Model

Abstract: The process of producing a succinct synopsis of a text involves two primary techniques: both abstractive and extractive While abstractive summarising rephrases text in a way that is more human-like, extractive summarization chooses important statements straight. handbook Because it takes time and money to summarise because it requires reading through big texts, an automated text model with lots of features It is suggested to use both extractive and abstractive methods for summarising. Important sentences that are enhanced by linguistic features are identified by a feature-rich extractor. These sentences are then further enhanced by an abstracter that adds components such named entity tags and part-of-speech tags. Moreover, a loss function is presented to match attentions at the word and sentence levels. At last the summarized text is converted into image by using parsing and other relatable techniques.

Summarization and Translation Using NLP

Abstract: Text summarization and translation are two critical tasks in natural language processing with significant applications in various domains such as news aggregation, document summarization, machine translation, and information retrieval. In recent years, there has been remarkable progress in the development of techniques and models for both tasks, leveraging advancements in deep learning and neural network architectures. This paper presents a comprehensive review and comparative analysis of state-of-the-art methods in text summarization and translation. First, we provide an overview of the different approaches to text summarization, including extractive, abstractive, and hybrid methods, highlighting their strengths and weaknesses. We discuss various evaluation metrics and datasets commonly used for benchmarking summarization systems, shedding light on the challenges and opportunities in this field.

Implementation Paper on Dermatological Proriasis and other Skin Diseases Diagnosis Using Machine Learning

Abstract: This paper "Dermatological Psoriasis and Other Skin Diseases Diagnosis Using Machine Learning" aims to develop an efficient and accurate system for identifying psoriasis, a chronic skin condition, through the utilization of machine learning techniques. Psoriasis affects millions worldwide, presenting challenges in its diagnosis and treatment. Leveraging advanced algorithms, this project seeks to analyse various skin images and clinical data to create a robust model capable of distinguishing psoriatic lesions from other dermatological conditions with high precision. By employing machine learning algorithms such as convolutional neural networks (CNNs) and support vector machines (SVMs), alongside feature extraction methods, the system aims to automate and streamline the diagnostic process, potentially reducing both time and burdens on healthcare professionals. Ultimately, the implementation of such a tool could significantly enhance early detection rates, improve patient outcomes, and facilitate more targeted treatment strategies for individuals affected by psoriasis

Cost analyses for malaria molecular diagnosis for research planners in India and beyond

ABSTRACT Background Malaria elimination mandates early and accurate diagnosis of infection. Although malaria diagnosis is programmatically dependent on microscopy/RDTs, molecular diagnosis has much better diagnostic accuracy. Higher cost of molecular diagnoses is a recognized challenge for use at the point of care. Because funding is always a recognized constraint, we performed financial cost-analyses of available molecular platforms for better utilization of available budget. Methods Two strategies were applied to deduce the cost per sample. Strategy 1 included recurring components (RC) in minimum pack size, and biologist’s time whereas strategy 2 included only RC and non-recurring components and costs are calculated for sample sizes (1–1,000,000) to infer the sample size effect. Results Spin column-based manual DNA extraction (US$ 3.93 per sample) is the lowest-cost method, followed by magnetic bead-based automated, semi-automated, and PCI-based manual method. Further, DNA extraction cost per sample via spin column-based manual method and semi-automated method decreases with an increase in sample size up to 10,000. Real-time PCRs are ~ 2-fold more economical than conventional PCR, regardless of sample size. Conclusions This study is the first for malaria to estimate systematic molecular diagnosis financial costs. Kit-based and automated methods may replace conventional DNA extraction and amplification methods for a frugal high-throughput diagnosis.

WuMKG: a Chinese painting and calligraphy multimodal knowledge graph

Chinese Painting and Calligraphy (ChP&C) holds significant cultural value, representing integral aspects of both Chinese culture and global art. A considerable amount of ChP&C works are dispersed worldwide. With the emergence of digital humanities, a vast collection of cultural artifacts data is now available online. However, the online databases of these artifacts remain decentralized and diverse, posing significant challenges to their effective organization and utilization. Addressing this, our paper focuses on the Wu Men School of Painting and proposes a framework for constructing a multimodal knowledge graph for the ChP&C domain. We construct the domain ontology by analyzing the ChP&C knowledge schema. Then, we acquire knowledge from diverse data sources, including textual and visual information. To enhance data collection around collecting historical context and subject matter, we propose methods for seal extraction and subject extraction specific to ChP&C. We validate the effectiveness of these methods on the constructed dataset. Finally, we construct the Wu Men Multimodal Knowledge Graph (WuMKG) and implement applications such as cross-modal retrieval, knowledge-based question-answering and visualization.

Synergistic corrosion inhibition of rubber seed extract with KI on cold rolled steel in sulfuric acid solution

BackgroundPlant-based inhibitor has emerged an important topic owing to the combing merits of eco-friendly, low cost, rich natural resources as well as easy preparation. Rubber seed is the agricultural waste, and is mainly composed of oil and fatty acids those contain many polar oxygen atoms, and so it can be extracted to prepare the potential corrosion inhibitor. MethodsRubber seed extract (RSE) is prepared using reflux extraction method with the extracting solvent of ethanol aqueous solution. The synergistic inhibition effect of RSE and potassium iodide (KI) on the corrosion of cold rolled steel (CRS) in 0.50 M H2SO4 solution is firstly studied by weight loss, potentiodynamic polarization (PDP) curves and electrochemical impedance spectroscopy (EIS) methods. CRS surface was thoroughly characterized using metallographic microscope, scanning electron microscope (SEM), atomic force microscope (AFM), X-ray photoelectron spectroscopy (XPS) and contact angle measurements. FTIR, UV–vis and liquid chromatography mass spectrometry (LC-MS) compositional analysis of RSE were used to analyze the efficient components in RSE. Significant findingsThe results indicate that individual RSE or KI cannot function as a successful inhibitor to prevent CRS corrosion, but the mixture of RSE/KI performs better at inhibiting, with a maximal inhibition efficacy of up to 96.4% of 200 mg L−1 RSE/100 mg L−1 KI. The synergistic coefficient of the mixture is larger than 1, indicating that a true synergism. RSE/KI can drastically retard both cathodic and anodic processes, and interfacial capacitance falls as the charge transfer resistance of CRS/solution rises. It exhibits remarkable inhibition properties when the capacitor arc grows. There is only a small corrosion response in the inhibited media, as evidenced by the variations in electrical conductivity and ultraviolet spectrum before and after immersion. The hydrophilicity and surface roughness of CRS surface is dropped as a result of the introduction of RSE/KI. A series of metallurgical microscopy, AFM, SEM or contact angle tests indicate a synergistic inhibition effect is more pronounced for RSE/KI. XPS results clearly demonstrate that elements including Fe, S, N, and I are bonded to the surface of CRS. The primary ingredients of oleic acid, polyphenols, phenylpropanoids can also produce a strong synergism with KI.

Ultrasound and pulsed electric field treatment effect on the thermal properties, oxidative stability and fatty acid profile of oils extracted from berry seeds

AbstractThe seeds of berry fruits which are considered as a by-product may be valorized by recovering the oil they content. Nevertheless, the conventional extraction methods are time, energy and organic solvents consuming. In order to make extraction more environmental-friendly, alternative methods, like ultrasound-assisted and pulsed electric field-assisted processes, are being developed. The procedure of extraction may however influence the quality of obtained oil. The following study aims to define the effects of ultrasound and pulsed electric field application in the extraction process on the thermal properties, i.e., oxidative stability, melting and cyclic heating/cooling profiles, thermal decomposition characteristics. Additionally, fatty acid profile assessment was included in the study. Seeds of blackberries, blackcurrants, chokeberries, raspberries and redcurrants were used to extract oil. Based on the results, it can be summarized that extraction method influenced the resistance of oils to oxidation. Ultrasound-assisted process resulted in oils with the highest oxidation induction times. Melting profile was slightly influenced by extraction method, with peak temperatures indicating the presence of low-melting and middle-melting triacylglycerol fractions. Differential scanning calorimetry with heat/cool/heat procedure let determine crystallization peak temperatures around − 60 °C, which could be associated with the specific triacylglycerol profile of berry seed oils. The courses of thermogravimetric analysis curves were comparable for all the tested samples. The fatty acid profile study revealed that all the studied thermal properties were affected by the unique fatty acid percentage share, with a great predominance of polyunsaturated fatty acids. Obtained results allowed to conclude that extraction method influenced oxidative stability, thermal properties and fatty acid profile only to some extent. The most promising extraction method among analyzed seems to be ultrasound-assisted extraction as it provided oil with high oxidative stability, typical thermal properties and unchanged fatty acid profile, without being harmful to natural environment due to possible reduction in solvent and time consumption.

Oral Administration Properties Evaluation of Three Milk-derived Extracellular Vesicles Based on Ultracentrifugation Extraction Methods.

Milk-derived extracellular vesicles (M-EVs) are low-cost, can be prepared in large quantities, and can cross the gastrointestinal barrier for oral administration. However, the composition of milk is complex, and M-EVs obtained by different extraction methods may affect their oral delivery. Based on this, we propose a new method for extracting M-EVs based on cryogenic freezing treatment (Cryo-M-EVs) and compare this method with the previously reported acetic acid treatment (Acid-M-EVs) method and the conventional ultracentrifugation method (Ulltr-M-EVs). The new method simplifies the pretreatment step and achieves 25-fold and 2-fold higher yields than Acid-M-EVs and Ulltr-M-EVs. And it was interesting to note that Cryo-M-EVs and Acid-M-EVs had higher cellular uptake efficiency, and Cryo-M-EVs presented the best transepithelial transport effect. After oral administration of the three M-EVs extracted by three methods in mice, Cryo-M-EVs effectively successfully crossed the gastrointestinal barrier and achieved hepatic accumulation, whereas Acid-M-EVs and Ultr-M-EVs mostly resided in the intestine. The M-EVs obtained by the three extraction methods showed a favorable safety profile at the cellular as well as animal level. Therefore, when M-EVs obtained by different extraction methods are used for oral drug delivery, we can utilize their accumulation properties at different sites to better deal with different diseases. This article is protected by copyright. All rights reserved.

Characteristics of lipid metabolism after treatment of colon cancer mice with American ginseng vesicles.

Lipid molecules are present in tumours and play an important role in the anti-inflammatory response as well as in antiviral protection. Changes in the type and location of lipids in the intestine following exposure to environmental stressors play an important role in several disorders, including ulcerative colitis (UC), inflammatory bowel disease (IBD), and colorectal cancer. The aim of this work is to provide a new theoretical basis for tumour initiation and development by accurately measuring the spatial distribution of lipids and metabolites in intestinal tissue. Spatial metabolomics allows the detection of samples with minimal sample volume by label-free imaging of complex samples in their original state. The distribution of lipid molecules in tumours has not been reported, although the distribution of lipid molecules in intestinal tissue has been reported in the literature. The range of lipid profiles in colon cancer mouse tumour tissue was compiled using a spatial metabolomics: lipid extraction method. The changes in lipid distribution in two regions after oral administration of American Ginseng (Panax quinquefolius L.) vesicles were also compared. Tumour tissue samples were extracted with 80% methanol-20% formic acid in water. The resulting spatial metabolic profile allowed the identification of seven lipid classes in mouse tumours. The distribution of fibre tissue cells was 23.2% higher than tumour tissue cells, with the exception of the fatty acid (FA) species.

Fault feature extraction method based on maximum envelope spectrum PFGI2 and empirical wavelet transform

Fast kurtogram (FK) is an efficient method for processing non-stationary signals, widely recognized by scholars as a rapid and effective approach for fault diagnosis. However, it has limitations in distinguishing between periodic pulse and random interference pulses due to the drawbacks in its frequency band segmentation methods and the inherent shortcomings of the kurtosis index itself. To address this, this paper proposes a fault feature extraction method based on the maximum envelope spectrum power function-based Gini indices (PFGI2) and empirical wavelet transform. This method, inspired by the concept of FK, constructs a series of band-pass filters following the principles of empirical wavelet decomposition. It applies envelope spectrum analysis to a series of sub-bands and calculates the PFGI2 value for each, to identify the optimal sub-band. The effectiveness of the proposed method is validated through simulations of vibration signals and experimental data.

A Feature Extraction Method for Prognostic Health Assessment of Gas Compressor Valves

Abstract This paper presents features derived from the pressure-volume (PV) diagram that is useful in estimating different valve faults in reciprocating compressors with a strong potential of remaining useful life prediction. The PV diagram is expected to deviate depending on valve wear conditions. The valve degradation types explored in this work are leakage, seat wear, and spring fatigue in the suction and discharge assemblies of a Dresser-Rand ESH-1 compressor commonly used in the petrochemical industry. The proposed method estimates the polytropic exponent on the compression and expansion phase as well as the discharge and suction valve loss power and uses them as features for a quadratic discriminant analysis. The features are created through in-cylinder pressure, suction pressure, discharge pressure, and crank angle measurements collected on a single-stage, dual-acting compressor operating on air with wear precisely machined and seeded into the poppets of the inlet and outlet valves. A very high classification accuracy is achieved in distinguishing the wear types, severity, and location with strong prognostic trends.