Extraction Methodology Research Articles

New Methodology for Parasitic Resistance Extraction and Capacitance Correction in RF AlGaN/GaN High Electron Mobility Transistors

This paper presents a novel approach to the efficient extraction of parasitic resistances in high electron mobility transistors (HEMTs). The study reveals that the gate resistance value can be accurately determined under specific forward gate bias conditions (Vg = 1.0 V), although the gate resistance value becomes unreliable beyond this threshold (Vg > 1.0 V) due to potential damage to the Schottky contact. Furthermore, by examining the characteristics of the device under a cold-FET bias condition (Vds = 0 V), a linear correlation between the gate and drain current is identified, enabling an estimation of the interdependence between the drain and source resistance using the proposed method. The estimation of parasitic pad capacitance (Cpg and Cpd) from Dambrine’s model is refined by incorporating the depletion layer capacitance on the gate side during the pinch-off condition. To validate the accuracy of the extracted parasitic capacitance and resistance values obtained from the new method, small-signal modeling is performed on a diverse range of measured devices.

Microplastics (≤ 10 μm) bioaccumulation in marine sponges along the Moroccan Mediterranean coast: Insights into species-specific distribution and potential bioindication

Microplastics (MPs) are pervasive in marine environments and widely recognized as emerging environmental pollutants due to the multifaceted risks they exert on living organisms and ecosystems. Sponges (Phylum Porifera) are essential suspension-feeding organisms that may be highly susceptible to MPs uptake due to their global distribution, unique feeding behavior, and sedentary lifestyle. However, the role of sponges in MP research remains largely underexplored. In the present study, we investigate the presence and abundance of MPs (≤10 μm size) in four sponge species, namely Chondrosia reniformis, Ircinia variabilis, Petrosia ficiformis, and Sarcotragus spinosulus collected from four sites along the Mediterranean coast of Morocco, as well as their spatial distribution. MPs analysis was conducted using an innovative Italian patented extraction methodology coupled with SEM-EDX detection. Our findings reveal the presence of MPs in all collected sponge specimens, indicating a pollution rate of 100%. The abundance of MPs in the four sponge species ranged from 3.95×105 to 1.05×106 particles per gram dry weight of sponge tissue, with significant differences observed among sampling sites but no species-specific differences. These results imply that the uptake of MPs by sponges is likely influenced by aquatic environmental pollution rather than the sponge species themselves. The smallest and largest MPs were identified in C. reniformis and P. ficiformis, with median diameters of 1.84 μm and 2.57 μm, respectively. Overall, this study provides the first evidence and an important baseline for the ingestion of small MP particles in Mediterranean sponges, introducing the hypothesis that they may serve as valuable bioindicators of MP pollution in the near future.

Presence and ecotoxicity of residual antidepressants in environmental samples

The consumption of antidepressants by the world population, especially during the pandemic of COVID-19, has increased. The increase in the use of these substances is intrinsically linked to their environmental detection. In this regard, the present work aims to evaluate the presence of the antidepressants venlafaxine, citalopram, fluoxetine, and sertraline in the Piracicaba River and to perform acute and chronic toxicity tests in Daphnia magna with the environmental concentration detected. The samples were extracted by solid phase extraction (SPE) methodology and analyzed by high-performance liquid chromatography coupled to a mass spectrometer with an electrospray ionization source. The highest environmental concentration detected for the acute and chronic tests with the individual antidepressants and their mixtures was used. The parameters used in the acute test were immobility and mortality, while in the chronic test were reproduction, survival, and dorsal and ventral size of D. magna. The acute risk quotient (RQ) was also estimated for each substance. The detection method was precise. The concentrations were determined to range from 1.0 to 4.48 ng L−1. The determined environmental concentrations do not present an acute risk for D. magna. According to the calculated RQ, antidepressants do not pose an acute risk to D. magna. The mixture of antidepressants using ambient concentrations did not cause dead or immobilized organisms. Reproduction and dorsal size of D. magna were affected by individual antidepressants and their mixtures. Antidepressants are present in the freshwater environment and can cause changes in non-target organisms; more studies are needed to verify the real impact of these substances on the entire trophic chain.

Extraction Methodologies and Applications of Renewable Polymers

Since there are large scales of production in modern industries all over the world, non-biodegradable materials would cause serious environmental issues when dealing with pollution treatments. To reduce environmental impact, it is said that producing renewable polymers is an effective method to maintain production yields without harming the environment. Renewable polymers are polymeric materials derived from renewable carbon wastes, and reused or biodegraded at the end of life cycles. This article introduces four examples of common renewable polymers with different bio-based platforms by comparing their structures, extraction methodologies, and applications respectively. The opportunities for sustainable polymer development would be based on performances while applying to industrial and commercial use. Although some renewable polymers are still not widely used on a grand scale due to yields or costs, the researchers would continue to investigate methods to improve the situation. Because of the appealing advantages of the production of renewable polymers, the future of manufacturing would be significantly beneficial to both environment and the whole economy.

Efficacy of three methods of extracting DNA from skeletal remains obtained from three taphonomic contexts

IntroductionThe extraction techniques of deoxyribonucleic acid (DNA) from old bone samples are based on the use of complex methodologies, which deal with the degradation of the sample, the low amount of DNA, and the presence of inhibitors that can be extracted simultaneously. ObjectiveTo compare the efficiency in obtaining DNA from the methods: organic extraction, commercial kit, and previous total demineralization, to obtain STR profiles of old bone samples from 3 taphonomic processes (alkaline, acid, and moist mitotic). Materials and methods29 skeletal remains from 3 different taphonomic contexts were processed: acidic, alkaline, and wet-fungal. The amount of DNA obtained from 3 methodologies was evaluated in a comparative manner: organic extraction method (phenol–chloroform–isoamyl alcohol) (SO), extraction by silica column KIT QIAamp® DNA Investigator of QIAgen® (KC), and the methodology of extraction by previous total demineralization (DP). Finally, the obtaining of the STR profiles from the methodology of greater performance was tested. ResultsThe following quantification values were obtained: (i) alkaline medium: 0.068±0.07 ng/μL (SO), 0.021±0.01 ng/μL (KC), and 0.073±0.052 ng/μL (DP); (ii) acidic medium: 0.098±0.064 ng/μL (SO), 0.041±0.029 ng/μL (KC), and 0.068±0.042 ng/μL; (iii) wet-mitotic medium: 0.25±0.061 ng/μL (SO), 0.04±0.027 ng/μL (KC), and 0.15±0.072 ng/μL (DP). Likewise, using the DNA samples obtained by the SO method, complete profiles were obtained for the wet taphonomic context while the alkaline taphonomic process proved to be the most drastic for DNA degradation, presenting a greater number of incomplete profiles. ConclusionsThe methodology of organic extraction was optimal in obtaining DNA from the 3 taphonomic processes evaluated. On the other hand, the wet-fungal taphonomic process is the one that produces the least negative impact on the preservation of DNA from skeletal remains.

Electrical Modeling of Spin-Torque Diodes Used as Radio Frequency Detectors: A Step-by-Step Methodology for Parameter Extraction

In this article here below, we present an electrical model for nanoscale spintronic radio frequency (RF) detectors called spin-torque diodes (STDs). A complete methodology for model parameter extraction is proposed. An equivalent electrical circuit for STD is derived and includes a modeling of the device resistance nonlinearity together with the STD effect. A detailed step-by-step methodology is presented to extract the model parameters using conventional dc measurements, RF scattering parameters ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$</tex-math> </inline-formula> -parameters), continuous wave (CW), and power characterizations. After parameter extraction, a comparison with measurements of a single STD successfully validates the model. Finally, the proposed STD electrical model is used to anticipate the behavior of a two-STD-based RF detector architecture. Simulation results highlight the interest of the proposed modeling approach to investigate suitable RF detector architectures to enhance RF-to-dc conversion efficiency for single or multiband RF detection.

"Integrating Location Information as Geohash Codes in Convolutional Neural Network-Based Satellite Image Classification"

In the past few years, there have been many research studies conducted in the field of Satellite Image Classification. The purposes of these studies included flood identification, forest fire monitoring, greenery land identification, and land-usage identification. In this field, finding suitable data is often considered problematic, and some research has also been done to identify and extract suitable datasets for classification. Although satellite data can be challenging to deal with, Convolutional Neural Networks (CNNs), which consist of multiple interconnected neurons, have shown promising results when applied to satellite imagery data. In the present work, first we have manually downloaded satellite images of four different classes in Florida locations using the TerraFly Mapping System, developed and managed by the High Performance Database Research Center at Florida International University. We then develop a CNN architecture suitable for extracting features and capable of multi-class classification in our dataset. We discuss the shortcomings in the classification due to the limited size of the dataset. To address this issue, we first employ data augmentation and then utilize transfer learning methodology for feature extraction with VGG16 and ResNet50 pretrained models. We use these features to classify satellite imagery of Florida. We analyze the misclassification in our model and, to address this issue, we introduce a location-based CNN model. We convert coordinates to geohash codes, use these codes as an additional feature vector and feed them into the CNN model. We believe that the new CNN model combined with geohash codes as location features provides a better accuracy for our dataset.

Combining high pressure and electric fields towards Nannochloropsis oculata eicosapentaenoic acid-rich extracts

Nannochloropsis oculata is naturally rich in eicosapentaenoic acid (EPA). To turn this microalga into an economically viable source for commercial applications, extraction efficiency must be achieved. Pursuing this goal, emerging technologies such as high hydrostatic pressure (HHP) and moderate electric fields (MEF) were tested, aiming to increase EPA accessibility and subsequent extraction yields. The innovative approach used in this study combined these technologies and associated tailored, less hazardous different solvent mixtures (SM) with distinct polarity indexes. Although the classical Folch SM with chloroform: methanol (PI 4.4) provided the highest yield concerning total lipids (166.4 mglipid/gbiomass), diethyl ether: ethanol (PI 3.6) presented statistically higher values in terms of EPA per biomass, corresponding to 1.3-fold increase. When SM were used in HHP and MEF, neither technology independently improved EPA extraction yields, although the sequential combination of technologies did result in 62% increment in EPA extraction. Overall, the SM and extraction methodologies tested (HHP—200 MPa, 21 °C, 15 min, followed by MEF processing at 40 °C, 15 min) enabled increased EPA extraction yields from wet N. oculata biomass. These findings are of high relevance for the food and pharmaceutical industries, providing viable alternatives to the “classical” extraction methodologies and solvents, with increased yields and lower environmental impact.Key points• Et2O: EtOH is a less toxic and more efficient alternative to Folch solvent mixture• HHP or MEF per se was not able to significantly increase EPA extraction yield• Combinations of HHP and MEF technologies increased both lipids and EPA yieldsGraphical abstract

Pilot-scale bioethanol production from the starch of avocado seeds using a combination of dilute acid-based hydrolysis and alcoholic fermentation by Saccharomyces cerevisiae

BackgroundA processing methodology of raw starch extraction from avocado seeds (ASs) and a sequential hydrolysis and fermentation bioprocess in just a few steps was successfully obtained for the bioethanol production by a single yeast Saccharomyces cerevisiae strain and this research was also to investigate the optimum conditions for the pretreatment of biomass and technical procedures for the production of bioethanol. It successfully resulted in high yields and productivity of all the experiments from the laboratory scale and the pilot plant. Ethanol yields from pretreated starch are comparable with those in commercial industries that use molasses and hydrolyzed starch as raw materials.ResultsBefore the pilot-scale bioethanol production, studies of starch extraction and dilute sulfuric acid-based pretreatment was carefully conducted. The amount of starch extracted from dry and fresh avocado seed was 16.85 g ± 0.34 g and 29.79 ± 3.18 g of dry starch, representing a yield of ∼17% and 30%, respectively. After a dilute sulfuric acid pretreatment of starch, the released reducing sugars (RRS) were obtained and the hydrolysate slurries containing glucose (109.79 ± 1.14 g/L), xylose (0.99 ± 0.06 g/L), and arabinose (0.38 ± 0.01 g/L). The efficiency of total sugar conversion was 73.40%, with a productivity of 9.26 g/L/h. The ethanol fermentation in a 125 mL flask fermenter showed that Saccharomyces cerevisiae (Fali, active dry yeast) produced the maximum ethanol concentration, pmax at 49.05 g/L (6.22% v/v) with a yield coefficient, Yp/s of 0.44 gEthanol/gGlucose, a productivity or production rate, rp at 2.01 g/L/h and an efficiency, Ef of 85.37%. The pilot scale experiments of the ethanol fermentation using the 40-L fermenter were also successfully achieved with essentially good results. The values of pmax,Yp/s, rp, and Ef of the 40-L scale were at 50.94 g/L (6.46% v/v), 0.45 gEthanol/gGlucose, 2.11 g/L/h, and 88.74%, respectively. Because of using raw starch, major by-products, i.e., acetic acid in the two scales were very low, in ranges of 0.88–2.45 g/L, and lactic acid was not produced, which are less than those values in the industries.ConclusionsThe sequential hydrolysis and fermentation process of two scales for ethanol production using the combination of hydrolysis by utilizing dilute sulfuric acid-based pretreatment and fermentation by a single yeast Saccharomyces cerevisiae strain is practicable and feasible for realistic and effective scale-up strategies of bioethanol production from the starch of avocado seeds.

Applying Gel-Supported Liquid Extraction to Tutankhamun's Textiles for the Identification of Ancient Colorants: A Case Study.

The identification of the dyes present on a linen fragment from the tomb of Pharaoh Tutankhamun is the objective of the present study. Fiber optic reflectance spectroscopy (FORS) was applied to the archaeological sample for preliminary identification of the dyes and to better choose the extraction methodology for different areas of the sample. The innovative gel-supported micro-extraction with agar gel and the Nanorestore Gel® High Water Retention (HWR) gel were applied to the archaeological sample after testing of the best concentration for the extraction of the agar gels substrates, performed on laboratory mock-ups by means of UV-Vis transmittance spectroscopy. Immediately after extraction, Ag colloidal pastes were applied on the gel surface and Surface Enhanced Raman Scattering (SERS) analysis was performed directly on them. The combination of information deriving from FORS and SERS spectra resulted in the successful identification of both indigo and madder and, in hypothesis, of their degradation products.

An innovative method for the extraction and HPLC analysis of bioactive policosanols from non-psychoactive Cannabis sativa L.

Policosanols (PCs) refer to a mixture of long-chain aliphatic alcohols. Sugar cane is the main industrial source of PCs, but others, including beeswax and Cannabis sativa L., are also known. In the raw material PCs are bonded to fatty acids to form long-chain esters, known as waxes.PCs are mainly used as a cholesterol-lowering product, even though their efficacy is controversial. More recently, the pharmacological interest in PCs has increased, as they have been investigated as antioxidant, anti-inflammatory and anti-proliferative agents.Given their promising biological implications, the development of efficient extraction and analytical methodologies for the determination of PCs is extremely important to identify new potential sources of these compounds and to ensure the reproducibility of biological data. Conventional techniques used for the extraction of PCs involve time-consuming approaches leading to low yields, while analytical methods for their quantification are based on gas-chromatographic (GC) techniques, which require an additional derivatization step during the sample preparation to increase their volatility.In the light of all the above, this work was aimed at the development of an innovative method for the extraction of PCs from non-psychoactive C. sativa (hemp) inflorescences, taking advantage of the microwave-assisted technology. In addition, a new analytical method based on high-performance liquid chromatography (HPLC) coupled with an evaporative light scattering detector (ELSD) was developed for the first time for both the qualitative and quantitative analysis of these compounds in the extracts. The method was validated according to ICH guidelines, and it was applied to the analysis of PCs in hemp inflorescences belonging to different varieties. The results were analyzed using Principal Component Analysis (PCA) and hierarchical clustering analysis to rapidly identify samples with the highest content of PCs, which might find an application as alternative sources of these bioactive compounds in both the pharmaceutical and nutraceutical fields.

Optimization of Total Phenolic Content Analysis of Chayote (Sechium edule) using Ultrasonic-Assisted Extraction and Response Surface Methodology

Chayote (Sechium edule) is a plant from the Cucurbitaceae family that contains several vitamins such as vitamins A, B, and C, and is often used for medicinal purposes. Based on phytochemical analysis, chayote extract contains bioactive compounds in the form of phenolics. Phenolic compounds are secondary metabolites and are used as cosmetic and medicinal ingredients due to their ability to reduce oxidative stress. This research aims to determine the total phenolic content and antioxidant activity of chayote (Sechium edule) extract using UV-Vis spectrophotometry. Chayote extract was obtained using the Ultrasonic Assisted-Extraction (UAE) method with 96% ethanol solvent. The extraction variables used were time (15, 20, and 25 minutes), amplitude (45, 65, and 85%), and solvent ratio (1:5, 1:10, and 1:15). The determination of total phenolic content was based on the Folin-Ciocalteau method, while antioxidant activity was determined using DPPH. The determination of chayote extraction parameters for total phenolic content was carried out using the Response Surface Methodology with a Box-Behnken type. The optimal conditions obtained for chayote extraction were 1.327 mg GAE/g sample for total phenolic content and 29.45% for antioxidant activity under the conditions of 20 minutes extraction time, 65% amplitude, and 1:10 solvent ratio.

Theoretical Investigation of Actinide Ligation in Aqueous and Organic Phase for Nuclear Waste Treatment.

The generation of radioactive waste has a prominent negative impact on the use of nuclear energy due to potential health concerns and cost of waste storage. This potential impact continues to rise as the quantity of waste increases due to the increasing growth in energy demand. One of the leading contributions to the radioactivity of this waste is due to the presence of actinides. The removal of these actinides by ligand-based solvent extraction methodologies provides an invaluable process necessary for the promotion of nuclear energy. By evaluating different ligands that are currently applied for actinide solvent extractions, more effective ligands could be proposed and synthesized for the successful separation of actinides from nuclear waste. Here, density functional theory (DFT) calculations for a variety of ligands and actinides are reported to explain the extraction process. Different solvation and ligation effects were evaluated for the computation of stability constants. The ratio of water and nitrate ligands in the coordination environment of actinides (Ac(III), Th(IV), Am(III), and Cm(III)) was first examined. Results from this step provided reliable initial conditions for the extraction of these actinides in both the aqueous (343HOPO) and the organic phase (BTBP). We also report a DFT benchmarking study as well as a modified BTBP ligand performance evaluation.

Methodological considerations for aqueous environmental RNA collection, preservation, and extraction.

Environmental RNA (eRNA) analysis is expected to infer species' physiological information (health status, developmental stage, and environmental stress response) and their distribution and composition more correctly than environmental DNA (eDNA) analysis. With the prospect of such eRNA applications, there is an increasing need for technological development for efficient eRNA detection because of its physicochemical instability. The present study conducted a series of aquarium experiments using zebrafish (Danio rerio) and validated the methodologies for capture, preservation, and extraction of eRNA in a water sample. In the eRNA extraction experiment, an approximately 1.5-fold increase in lysis buffer volume resulted in a more than sixfold increase in target eRNA concentration. In the eRNA capture experiment, although GF/F and GF/A filters yielded similar eRNA concentrations, a GF/A filter may be capable of passing through more volume of water samples and consequently collecting more eRNA particles, given the time required for water filtration. In the eRNA preservation experiment, the use of RNA stabilization reagent (RNAlater) allowed for stably preserving target eRNA on a filter sample at -20 and even 4°C for 6days at least. Altogether, the findings enable the improvement of eRNA availability from the field and easily preserve eRNA samples without deep-freezing, which will contribute to the refinement of eRNA analysis for biological and physiological monitoring in aquatic ecosystems.

ACO-KELM: Anti Coronavirus Optimized Kernel-based Softplus Extreme Learning Machine for classification of skin cancer

Due to the presence of redundant and irrelevant features in large-dimensional biomedical datasets, the prediction accuracy of disease diagnosis can often be decreased. Therefore, it is important to adopt feature extraction methodologies that can deal with problem structures and identify underlying data patterns. In this paper, we propose a novel approach called the Anti Coronavirus Optimized Kernel-based Softplus Extreme Learning Machine (ACO-KSELM) to accurately predict different types of skin cancer by analyzing high-dimensional datasets. To evaluate the proposed ACO-KSELM method, we used four different skin cancer image datasets: ISIC 2016, ACS, HAM10000, and PAD-UFES-20. These dermoscopic image datasets were preprocessed using Gaussian filters to remove noise and artifacts, and relevant features based on color, texture, and shape were extracted using color histogram, Haralick texture, and Hu moment extraction approaches, respectively. Finally, the proposed ACO-KSELM method accurately predicted and classified the extracted features into Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC), Actinic Keratosis (ACK), Seborrheic Keratosis (SEK), Bowen’s disease (BOD), Melanoma (MEL), and Nevus (NEV) categories. The analytical results showed that the proposed method achieved a higher rate of prediction accuracy of about 98.9%, 98.7%, 98.6%, and 97.9% for the ISIC 2016, ACS, HAM10000, and PAD-UFES-20 datasets, respectively.

Variable Clustered Fuzzy Rules for Self-Tuning Scheme for Respiratory Distresses by Similarity and the Phase Plane Trajectory Concept

This work proposes a fuzzy rule extraction methodology for a self-tuning fuzzy controller based on the fuzzy clustering method (FCM) and the similarity approach technique. The similarity technique and phase plane trajectory method are used to even lower the acquired rules. To show a potential rule extraction scheme, the self-tuning fuzzy-logic-based proportional-derivative (STFLPDC) with 49 expert fuzzy rules and 49 clustered fuzzy gain rules is used. The utility of the approach is validated using common clustering validity indices. With 49 initial clustered fuzzy rules, the suggested method addressed oxygen supply in a human respiratory model. After further reduction using the similarity strategy, 29 and 14 fuzzy rules remained. Parallel to this, a real-time benchmark application using 49, 21, and 16 extracted fuzzy gain rules is used to access the performance of a lab-based overhead crane. Finally, to investigate control performance in both models, the phase plane trajectory concept is used to generate as few as 13 fuzzy gain rules.

A Critical Assessment of Extraction Methodologies for the Valorization of Agricultural Wastes: Polyphenolic Profile and Bioactivity

Different extraction techniques were used to exploit fruit processing residues for their use as a source of phenolic compounds. Three different extraction methods, namely microwave-assisted extraction (MAE), subcritical water extraction (SWE), and maceration (M), were assessed to gauge their respective efficacies. Total phenolic content (TPC) and total flavonoid content (TFC), ferric reducing antioxidant power (FRAP), and radical scavenging activity ABTS assay were evaluated. High-performance liquid chromatography was used to assess the polyphenolic profile. MAE was the extraction technique that allowed the highest recovery of polyphenolic compounds. Concerning the fruit by-products analyzed, the extract of pomegranate peels obtained using M60C and MAE had the highest TPC (313 ± 24 mg GAE/g dry weight (dw)) and TFC (36.0 ± 2.8 mg EE/g dw), respectively, and the highest antioxidant activity (FRAP = 740 ± 67 mg AAE/g dw and ABTS (628 ± 27 mg TE/g dw) corresponded to M60C. The phenolic composition obtained for this sample using high-performance liquid chromatographic–diode array detection (HPLC–DAD) showed that gallic acid, protocatechuic acid, β-resorcylic acid, (+)-cathechin, and rutin were the main phenolics found. The findings underscore the capacity of agricultural by-products to act as a source of phenolic compounds. This offers a feasible solution to enhance the nutritional content in food while simultaneously minimizing environmental waste.

How Do Alternative Protein Resources Affect the Intestine Morphology and Microbiota of Atlantic Salmon?

The availability and cost of fishmeal constitute a bottleneck in Atlantic salmon production expansion. Fishmeal is produced from wild fish species and constitutes the major feed ingredient in carnivorous species such as the Atlantic salmon. These natural stocks are at risk of depletion and it is therefore of major importance to find alternative protein sources that meet the nutritional requirements of the Atlantic salmon, without compromising the animals' health. Terrestrial animal by-products have been used in aquaculture feed, but their use is limited by the lack of several essential amino acids and consumer acceptance. In the case of plant ingredients, it is necessary to take into account both their concentration and the extraction methodologies, since, if not dosed correctly, they can cause macro- and microscopic alterations of the structure of the gastrointestinal tract and can also negatively modulate the microbiota composition. These alterations may compromise the digestive functions, growth of the animal, and, ultimately, its well-being. An updated revision of alternative protein sources is provided, with the respective impact on the intestine health in terms of both morphology and microbiota composition. Such information may constitute the premise for the choice and development of Atlantic salmon feeds that guarantee fish health and growth performance without having a significant impact on the surrounding environment, both in terms of depletion of the fish's natural stocks and in terms of pressure on the terrestrial agriculture. The sustainability of aquaculture should be a priority when choosing next-generation ingredients.

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part I: Bridges and Viaducts

Bridges and viaducts are critical components of railway transport infrastructures, providing safe and efficient means for trains to cross over natural barriers such as rivers and valleys. Ensuring the continuous safe operation of these structures is therefore essential to avoid disastrous economic consequences and even human losses. Drive-by methodologies have emerged as a potential and cost-effective monitoring solution for accurately and prematurely detecting damage based on instrumented vehicles while minimizing disruptions to train operations. This paper presents a critical review of drive-by methodologies applied to bridges and viaducts. Firstly, the premises of the method are briefly reviewed, and the potential applications are discussed. In sequence, several works involving the use of drive-by methodologies for modal characteristic extraction are presented, encompassing the most important methodologies developed over time as well as recent advancements in the field. Finally, the problem of damage identification is discussed—both in relation to modal and non-modal parameter-based techniques considering the most promising features and the current advancements in the development of methodologies for damage detection based on machine learning algorithms. A comprehensive conclusion is presented at the end of the article, summarizing the achievements and providing perspectives for future developments. By critically assessing the application of drive-by methodologies to bridges and viaducts, this paper contributes to the advancement of knowledge in this crucial area, emphasizing the significance of continuous monitoring for ensuring the integrity and safety of these vital transport infrastructures.

A survey on Relation Extraction

With the advent of the Internet, the daily production of digital text in the form of social media, emails, blogs, news items, books, research papers, and Q&A forums has increased significantly. This unstructured or semi-structured text contains a huge amount of information. Information Extraction (IE) can extract meaningful information from text sources and present it in a structured format. The sub-tasks of IE include Named Entity Recognition (NER), Event Extraction, Relation Extraction (RE), Sentiment Extraction, Opinion Extraction, Terminology Extraction, Reference Extraction, and so on.One way to represent information in the text is in the form of entities and relations representing links between entities. The Entity Extraction task identifies entities from the text, and the Relation Extraction (RE) task can identify relationships between those entities. Many NLP applications can benefit from relational information derived from natural language, including Structured Search, Knowledge Base (KB) population, Information Retrieval, Question-Answering, Language Understanding, Ontology Learning, etc. This survey covers (1) basic concepts of Relation Extraction; (2) various Relation Extraction methodologies; (3) Deep Learning techniques for Relation Extraction; and (4) different datasets that can be used to evaluate the RE system.