Fast Method Research Articles

Geotechnical engineering surveys and soil testing in the construction of buildings and structures

The article suggests a way to increase the efficiency of geotechnical engineering surveys through the rational use of fast approximate methods of soil testing (“express methods”). Such tests are recommended to be carried out in large volume and considered as a means of assessing the geotechnical structure of the site as a whole. In this paper, these issues are considered by the example of determining the bearing capacity of piles according to static sounding data and static tests of full-scale piles. A mathematical model for assessing the information content of the test complex based on the concepts of information theory is proposed. Calculations based on this model show that the informativeness of a large number of approximate tests may (due to the heterogeneity of the soil) exceed the informativeness of a small number of accurate tests.

Protein structure alignment by reseek improves sensitivity to remote homologs.

Recent breakthroughs in protein fold prediction from amino acid sequences have unleashed a deluge of new structures, presenting new opportunities and challenges to bioinformatics. Reseek is a novel protein structure alignment algorithm based on sequence alignment where each residue in the protein backbone is represented by a letter in a "mega-alphabet" of 85,899,345,920 (∼1011) distinct states. Reseek achieves substantially improved sensitivity to remote homologs compared to state-of-the-art methods including DALI, TMalign and Foldseek, with comparable speed to Foldseek, the fastest previous method. Scaling to large databases of AI-predicted folds is analyzed. Foldseek E-values are shown to be under-estimated by several orders of magnitude, while Reseek E-values are in good agreement with measured error rates. https://github.com/rcedgar/reseek. Supplementary data are available at Bioinformatics online.

Fast DOA Estimation of IGS Noncircular Signals Under Coprime Arrays

Traditional direction-of-arrival (DOA) estimation methods often encounter challenges such as low accuracy and high computational complexity, particularly in complex signal environments with multiple sources. To address these issues, this paper proposes a fast DOA estimation method for improper Gaussian signaling (IGS) non-circular signals, based on a coprime array; this approach involves extending the array model by leveraging the unique properties of non-circular signals. Subsequently, this study introduces the ANCM algorithm, a method designed for fast DOA estimation of IGS non-circular signals within a non-uniform noise background. The proposed ANCM algorithm is then compared to classical algorithms, through simulations in a non-uniform noise environment. Experimental results demonstrate that the ANCM algorithm significantly outperforms traditional methods in such conditions. Therefore, the development of new, efficient DOA estimation methods holds substantial theoretical and practical value.

Comparison of conventional and digital impression approaches for edentulous maxilla: clinical study.

The expectations for the clinical and laboratory phases of tissue-supported complete dentures (TSCDs) are changing. Currently, there is a trend towards fast, comfortable, reliable, and low-cost methods. In TSCD impressions, simplified impression and digital impression methods involving the use of an intraoral scanner (IOS) are becoming preferable. Given this situation, this study aims to compare different conventional and digital impression methods used in TSCDs. Patients with maxillary complete edentulism and healthy oral mucosa were included in this study. In the digital group, two different impressions were made using an IOS (Trios4) without (D1) and with (D2) artificial intelligence scanning (AI-Scan). In addition, a modified impression (D3) was made using the IOS in two steps, including the occlusal rim. In the conventional group, a two-step impression (C1) using an individual tray with zinc oxide eugenol and a one-step simplified impression (C2) using a stock tray with irreversible hydrocolloid were made. The comparison groups were determined to be C1-C2, C1-D1, D1-D2, and D1-D3. The best-fit algorithm was used to superimpose the impressions to be compared. The right and left vestibular areas, postdam area, palatal area, right and left matching area, entire surface, and borders were evaluated separately. Fifteen patients were included in this study. In the C1-C2 group, the mean deviation at the borders was statistically significant (p = 0.01). No regions in the C1-D1 and D1-D3 groups exhibited significant differences in the mean amount of deviation (p > 0,05). In the D1-D2 group, the mean deviation in the palatal area was significant (p = 0,03). In maxillary edentulism, digital impressions have shown comparable results to conventional impressions, suggesting promising implications for clinical applications. The clinical trial has been registered (ClinicalTrials.gov ID NCT06400277, registration date 06/05/2024, 'retrospectively registered').

RNA Blotting by Electrotransfer and RNA Detection.

Northern blot, or RNA blot, is a widely used technique in molecular biology to detect and semi-quantify specific RNAs. The advantage of this method is its ability to simultaneously estimate the sizes and quantities of degraded or processed RNA products. Northern blotting involves the use of electrophoresis to separate RNA samples by size. These RNAs are then transferred and immobilized on a membrane, and the RNAs of interest are detected by hybridization using a sequence-specific labeled DNA probe. Agarose gels are typically used in routine procedures to allow for the separation and specific detection of high molecular weights (ranging from tRNA size to several kb RNAs). However, acrylamide gels are preferred when the separation of lower weight RNAs (ranging from 20 to 200 nucleotides) is required. Both approaches for RNA separation are presented here. The term "northern blot" originally referred to the capillary transfer of RNA from the electrophoresis gel to the blotting membrane. However, RNA transfer to the membrane by electrotransfer has also been proven to be a suitable and fast method for transferring RNAs from acrylamide and agarose gels to the membrane.

Early detection of heartwood rot caused by Fistulina hepatica in Castanea sativa productive coppices through low-invasive resistance drilling

AbstractChestnut Red Stain (CRS) is a heartwood discoloration that widely affects Castanea sativa Mill. productive coppice stands in the northeast of Spain. At the early stages of infection, the structural properties of the wood are not affected, but still its economic value drops up to 70% due to the rejection of this wood. This disease is caused by the fungus Fistulina hepatica (Schaeff.) With. and causes uncertainty to forest managers since the fungal infection is virtually impossible to detect before felling. The objective of this work was to develop an efficient detection method to evaluate the presence of F. hepatica in early stages of productive periods. A total of 72 chestnuts were analyzed through molecular methods to determine the presence of F. hepatica and with an IML resistance drill to characterize their wood. Thirteen wood quality indices were calculated and their correlation with the presence of the pathogen was evaluated using linear mixed models. We found clear differences between healthy and infected trees in four indices. A new specific index (Chestnut Red Stain Danger index) was designed to estimate the probability of infection by F. hepatica. The results support the hypothesis that the early presence of F. hepatica is detectable through inexpensive and fast mechanical methods early in a rotation. The results of this work will help forest managers evaluate the incidence of CRS, as well as it establishes a novel methodology for further development of resistance drilling techniques for heartwood rot detection.

Fundamentals and Implication of Point of Zero Charge (PZC) Determination for Activated Carbons in Aqueous Electrolytes.

The point of zero charge (PZC) is a crucial parameter for investigating the charge storage mechanisms in energy storage systems at the molecular level. This paper presents findings from three different electrochemical techniques, compared for the first time: cyclic voltammetry (CV), staircase potentio electrochemical impedance spectroscopy (SPEIS), and step potential electrochemical spectroscopy (SPECS), for two activated carbons (ACs) with 0.1molL-1 aqueous solution of LiNO3, Li2SO4, and KI. The charging process of AC operating in aqueous electrolytes appears as a complex phenomenon - all ionic species take an active part in electric double-layer formation and the ion-mixing zone covers a wide potential region. Therefore, the so-called PZC should not be considered as an absolute one-point potential value, but rather as a range of zero charge (RZC). SPECS technique is found to be a universal and fast method for determining RZC, as applied here together with the EQCM. In most cases, the RZC covers a potential range from ≈100 to ≈200mV and the correlation of the range with the carbon microtexture is clear, highlighting the role of the ion-sieving effect. It is postulated that PZC for porous materials in aqueous electrolytic solutions should be considered instead as RZC.

Acoustic analysis of a three-dimensional cylindrical shell model under electromagnetic vibration

This paper presents the acoustic analysis of a three-dimensional cylindrical shell model under electromagnetic vibration, a critical factor affecting the performance of electric motors in various applications such as automotive, aerospace, and industrial systems. The study provides a multidisciplinary approach that integrates electromagnetics, structural vibration, and acoustics, solved using the fast multipole boundary element method (FMBEM). The results summarize the validation of the analytical models and numerical simulations, offering insights into effective vibration reduction methods. The conclusions indicate that the 3-D numerical analysis using FMBEM aligns well with the analytical solution for the sound pressure in the exterior acoustic domain of the cylindrical shell model. The paper contributes valuable insights for the design of low-noise motors and the control of electromagnetic vibration and noise in electric motors.

Abstract 4137103: Myofibril Mechanics and Transcriptomic Profiling Confirm Distinct Phenotype of TNNI3- Cardiomyopathy Mouse Model

Introduction: Cardiac troponin I (cTnI, TNNI3 gene) is a highly conserved subunit of the sarcomere that inhibits contraction by preventing actin-myosin interaction. Pathogenic TNNI3 variants can cause both hypertrophic and restrictive cardiomyopathies but lack targeted therapies. We identified a family with restrictive cardiomyopathy carrying a cTnI variant at amino acid 157 (A157V). Using CRISPR-Cas9, we generated a knock-in mouse model reflecting this mutation (A158V in mouse). Our previous studies showed that homozygous A158V mice had impaired cardiac relaxation on invasive hemodynamics but normal lifespan, no echocardiographic changes, and no fibrosis. Hypothesis: Myofibril mechanics and transcriptomic studies will better discriminate TNNI3 A158V mice from WT than previous phenotyping studies. Methods: Heart tissue was obtained from wild-type (WT) and homozygous A158V mice at 9-10 months. Myofibrils were isolated from heart tissues and mechanical measurements were completed using the fast solution switching method. RNA was extracted for bulk RNA sequencing. Results: Isolated myofibril studies showed significant prolongation of the linear relaxation phase in A158V mice versus controls (p=0.02) but no significant differences in active tension. Unsupervised clustering of bulk RNA samples separated A158V samples from controls, with 44% transcriptional variation derived from genotype. Cardiac changes in the A158V model were confirmed by significant depletion of pathways involved in structural components of the contractile apparatus, including contractile fiber, sarcomere, and I-Band. On an individual gene level, A158V mutated mice displayed higher expression of cardiac remodeling genes such as Timp1 , Postn , and Tnc . Conclusion: The TNNI3 A158V variant consistently produces impaired relaxation with myofibril studies showing prolonged linear relaxation phase. Transcriptomic studies highlight distinct clustering of TNNI3 A158V mice from WT, with prominent depletion in contractile apparatus pathways. Our findings suggest traditional methods to characterize mouse models may fail to capture disease phenotype in cardiomyopathies; however, myofibril studies and transcriptomic profiling may reveal earlier phenotypic changes.

A Fast Converter Synchronization Speed Method Based on the Frequency Difference on Both Sides of the Grid Connection Point

The challenge of achieving a reliable and safe synchronization process for microgrids under weak communication conditions is a significant issue in distributed grid-connected energy storage. This is also the core motivation of this study. First, the concept of weak communication is introduced, and weak communication conditions are simulated by limiting the number of communications. Additionally, a fast synchronization method based on the frequency difference at the grid connection point is proposed, which allows for the rapid synchronization of converters under weak communication conditions. This method also includes an analysis of the range for key parameters, providing practical and feasible guidance for its real-world application. Finally, the validity of the theory is verified through PSCAD/EMTDC simulations and physical experiments.

A Validated Trigonelline-Based Method for the Standardization and Quality Control of Trigonella foenum-graecum L.

Background Fenugreek, or Trigonella foenum-graecum L, is an edible and medicinal plant of the Fabaceae family. Fenugreek seeds contain a variety of phytochemicals, including proteins, lipids, amino acids, vitamins, flavonoids, steroidal saponins, coumarin, and alkaloids. Trigonelline TG is a bioactive plant alkaloid initially extracted from fenugreek seeds. A substantial portion of fenugreek’s health benefits may rely on the presence of TG. This study addresses the research gap for a fast, green, and economical method for quantifying trigonelline (TG) in fenugreek. Methods Fenugreek seeds from various origins were extracted using three green solvents: acetone (ACt), ethanol (EtOH), and water (H2O). The UPLCMSMS method was developed and validated using a green mobile phase of H2O: EtOH, and an r2-value of 0.999 in the linearity range of 0.1-500 ppb was adopted. The method was validated with an accuracy of 98.6% for trace analysis of TG using a small amount (10 mg) of fenugreek samples from five different origins. Results The average extract yield (±SD) was 5.36±6.3, with the highest extract yield observed in H2O. The ESI (+ve) of the UPLCMSMS resulted in the fragmentation pattern (m/z) 138→94.10→92.05→78.20. The TG quantification revealed an average TG concentration of 181.4, with the highest amount of TG in H2O extract (392.7±132.4 ppb), followed by EtOH (91.9±83.3 ppb) and ACt (59.5±30.9 ppb). The TG amount observed in the validation step substantiated the efficiency and reproducibility of the developed method. Conclusions The method may be used as an effective tool for a green, rapid, economical, and eco-friendly extraction and quantification of TG in diverse matrices of pharmaceutical, cosmeceutical, herbal, and food products.

BSO algorithm and artificial neural network aided emission optimisation for gas turbine engine

Abstract Aircraft play a major role in meeting the fast and efficient transportation needs of modern society, thanks to their advanced features. However, gas turbine engines used in aircraft have many negative effects on human health. One of the negative effects is the exhaust gases released by these engines to nature. In this study, it is discussed to present alternative models based on heuristic methods to reduce the emission values of the synthetic fuel mixture used in the combustion chamber of gas turbine engines. For this purpose, a model based on artificial neural networks (ANN) based on the back-tracking search optimisation (BSO) algorithm is proposed by using experimentally obtained emission values found in the literature. In the proposed model, the parameters of the optimum ANN structure are first determined by the BSO algorithm. Then, by using the optimum ANN structure, the most appropriate input values were found with the BSO algorithm, and the emission values were reduced. The simulation results have shown that the proposed method will be a fast and safe alternative method for reducing emission values.

A General Strategy to Rapid and Sensitive Determination of Five High Boiling Point Solvents in Pharmaceuticals by RP-HPLC Combined with Precipitation Method.

The difficulty on determination of high boiling point solvents, always exist due to their low vapor pressure in headspace (HS) vial. What's worse, the commonly used GC diluents, such as N, N-dimethylacetamide, 1,3-dimethyl-2-imidazolidinone, dimethyl sulfoxide, N, N-dimethylformamide and N-methylpyrrolidone are often remained in HS instrument after frequent injection into HS, thus would result in terrible positive interference problem. In this work, a fast and sensitive HPLC method in combination with a sample precipitation method, was proposed for the determination of above five high boiling point solvents in drug substances within 14.5min. The final method was validated with regard to limit of detection and limit of quantitation, precision, linearity and accuracy in accordance with ICH Q2 (R1). Finally, the proposed strategy was successfully applied for the analysis of high boiling point solvents used in three active pharmaceutical ingredients. The established generic strategy would provide another choice for the determination of high boiling point solvents in pharmaceuticals.

Measuring Microplastic Concentrations in Water by Electrical Impedance Spectroscopy

Plastics are vital for society, but their usage has grown exponentially and contributes to the growth of pollution worldwide. The World Health Organization, WHO, already reported that microplastics (MPs) are found everywhere, in waste and fresh water, and in the air and soil. Regarding water effluents, waste-water treatment plants only minimize the problem, trapping only larger size particles. In contrast, smaller ones remain in oxidation ponds or sewage sludges, or are even released to aquifers environment. Classic procedures for MPs detection are still quite laborious, and are usually conducted off-line, involving several steps and expensive equipment. Electrical Impedance Spectroscopy, EIS, is a technique that allows the analysis of a system’s electrical response, yielding helpful information about its domain-dependent on physical-chemical properties. Due to the superficial electronegativity of MPs’ particles, EIS may allow to attain the purpose of the present work: to provide a fast and reliable method to detect/estimate MPs’ concentration in water effluents. Among the most common microplastics are Polyethylene, PE, and Polyvinyl Chloride, PVC. Using the developed setup and experimental data collection methodology, the authors could differentiate between MPs’ suspensions containing the same concentration of the different evaluated MPs, PVC and PE, and assess PVC concentration variation, in the interval between 0.03 to 0.5 g (w/w), with an error, estimated based on the obtained impedance modulus, around or below 3% for the entire stimulus signal frequency range (from 100 Hz to 40 MHz) for the PVC particles.

Rapid Microfluidic Biosensor for Point-of-Care Determination of Rheumatoid Arthritis via Anti-Cyclic Citrullinated Peptide Antibody Detection

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that causes extensive damage to multiple organs and tissues and has no known cure. This study introduces a microfluidic detection platform that combines a microfluidic reaction chip with a micro-spectrometer to accurately detect the anti-cyclic citrullinated peptide antibody (anti-CCP Ab) biomarker, commonly associated with arthritis. The surface of the microfluidic reaction chip is functionalized using streptavidin to enable the subsequent immobilization of biotinylated-labeled cyclic citrullinated peptide (biotin–CCP) molecules through a streptavidin–biotin reaction. The modified chip is then exposed to anti-CCP Ab, second antibody conjugated with horseradish peroxidase (HRP) (2nd Ab-HRP), 3,3′,5,5′-tetramethylbenzidine (TMB), and a stop solution. Finally, the concentration of the anti-CCP Ab biomarker is determined by analyzing the optical density (OD) of the colorimetric reaction product at 450 nm using a micro-spectrometer. The detection platform demonstrated a strong correlation (R2 = 0.9966) between OD and anti-CCP Ab concentration. This was based on seven control samples with anti-CCP Ab concentrations ranging from 0.625 to 100 ng/mL. Moreover, for 30 artificial serum samples with unknown anti-CCP Ab concentrations, the biosensor achieves a correlation coefficient of (R2 = 0.9650). The proposed microfluidic detection platform offers a fast and effective method for accurately identifying and quantifying the anti-CCP Ab biomarker. Thus, it offers a valuable tool for the early diagnosis and monitoring of RA and its progression in point-of-care settings.

Development of low-cost, fast, and sensitive voltammetric methods for the determination of donepezil using unmodified carbon paste electrode

Development of low-cost, fast, and sensitive voltammetric methods for the determination of donepezil using unmodified carbon paste electrode

Human oocyte zona pellucida abnormalities: evaluation of clinical impact for different zona pellucida abnormalities and role of using assisted hatching.

Zona pellucida (ZP) plays an important role in oocyte development and fertilisation, as well as in early embryo development. However, there is currently no exploration of systematic categorising and clinical treatment measures for patients with abnormal ZP, especially the fast and easy method that can be used clinically. In this study, 185 patients with abnormal ZP (ZPA) and 222 patients with normal ZP (ZPN) were enrolled and analysed. Patients with ZPA exhibited altered hormone levels and lower laboratory outcomes in in vitro fertilisation (IVF), such as reduced MII rate, cleavage rate, high-quality embryo rate, and blastocyst formation rate compared to ZPN group. ZPA patients were sub-categorised into ZPA-A/B/C/D group according to the status of oocyte polar body and the width of perivitelline space (PVS). There are also differences in the degree of sperm binding to ZP derived from different ZPA patients and the elasticity of the oocyte membrane. ZPA subgroup analysis revealed further disparities in various IVF parameters and pregnancy outcomes. In addition, by performing different treatments on oocytes derived from ZPA patients, we found that ZP assisted hatching before the first oocyte cleavage on D1 of embryonic development in the ZPA-A/B groups and before blastocyst expansion on D4 of embryonic development in the ZPA-C/D groups were beneficial to improve embryonic development. The degree of the impact on clinical outcomes is correlated with the types of ZPA, and laser-assisted hatching of the ZP helps to improve embryonic development in patients with ZPA.

Exploring the Role of Artificial Intelligence in Revolutionizing Microbial Diagnostics

Identifying the microorganisms responsible for illnesses and infections is crucial for accurate diagnosis, which is an essential aspect of providing medical care. Artificial intelligence (AI) systems can enhance drug discovery, epidemiological monitoring, prediction of antibiotic resistance, and disease management in the field of microbiological diagnosis. Artificial intelligence (AI) is a branch of science and technology that uses computers to simulate human intelligence. AI has recently shown tremendous promise as a powerful computational tool for the detection and management of bacterial infections. These machines can imitate human thought processes and cognitive capacities. Today, pathogen identification and Antimicrobial Susceptibility Testing (AST) in clinical laboratories often rely on culturing and isolating pathogens. With the rapid advancement of technology, Artificial Intelligence (AI) has become a vital tool for bacterial AST, providing numerous fast and efficient methods for testing drug susceptibility. AI systems offer enhanced diagnostic methods and early detection of antibiotic resistance. Moreover, they can rapidly and accurately identify diseases, including those that are novel or drug-resistant. In addition, AI can be applied to identify cells infected with malaria, detect drug resistance in pathogens like tuberculosis (TB) and HIV, understand and tackle antimicrobial resistance (AMR), and predict outbreaks. The primary goals of applying AI to bacterial diagnosis are the rapid and precise identification of pathogens and the prediction of drug resistance.

Fast and explainable outlier detection in geotechnical database

ABSTRACT Outlier detection is an important task in data-driven geotechnics. It is noted that many existing methods for outlier detection (e.g. Bayesian learning, neural networks) may pose computational challenge for conventional geotechnical practitioners. Towards this aspect, this study proposes a simple, fast, and explainable method for detecting outliers in geotechnical database. The principle of the method is that for a data point to be labelled as a potential outlier, the probability of observing a data point as extreme as that value should be low. To account for outliers in left-tail and right-tail, the skewness of the dataset is incorporated, and an indicator referred to as outlier score ( > 0 ) is assigned to each data point. The method also provides another indicator (referred to as dimensional outlier score) to identify which dimensions/soil properties contribute to the outlierness; hence, it is explainable. The method doesn't require any time-consuming learning or sampling procedures; hence, it is quite fast and practitioner-friendly. Multiple numerical examples are utilised to demonstrate the capability first. Finally, four publicly available geotechnical databases are utilised to demonstrate the outlier detection task. The results suggest that the outliers identified using the proposed method can be meaningful from a geotechnical point of view.

Application of Binary Image Quality Assessment Methods to Predict the Quality of Optical Character Recognition Results

One of the continuous challenges related to the growing popularity of mobile devices and embedded systems with limited memory and computational power is the development of relatively fast methods for real-time image and video analysis. One such example is Optical Character Recognition (OCR), which is usually too complex for such devices. Considering that images captured by cameras integrated into mobile devices may be acquired in uncontrolled lighting conditions, some quality issues related to non-uniform illumination may affect the image binarization results and further text recognition results. The solution proposed in this paper is related to a significant reduction in the computational burden, preventing the necessity of full text recognition. Conducting only the initial image binarization using various thresholding methods, the computation of the mutual similarities of binarization results is proposed, making it possible to build a simple model of binary image quality for a fast prediction of the OCR results’ quality. The experimental results provided in the paper obtained for the dataset of 1760 images, as well as the additional verification for a larger dataset, confirm the high correlation of the proposed quality model with text recognition results.