Fast Analysis Research Articles

Evaluation of an innovative, open-source and quantitative approach for the kinematic analysis of rock slopes based on UAV based Digital Outcrop Model: A case study from a railway tunnel portal (Finale Ligure, Italy)

This paper presents a novel, cost-effective method for a rapid and quantitative characterization of discontinuities in fractured rock cliffs using Unmanned Aerial Vehicle-derived Digital Outcrop Models (DOMs). The proposed workflow combines manual extraction of discontinuity data from the DOM with a kinematic analysis using an automated algorithm (ROKA), which upgrades and renews the traditional Markland's test. The case study, a designed railway tunnel portal in NW Italy (Finale Ligure) demonstrates the advantages and limitations of this approach compared to current methods. Manual mapping is compared with (semi-)automatic extraction of discontinuity data from the DOM and validated with field-based scan lines. The results show that in complex geological settings, manual mapping provides more statistically robust and geologically reliable measurements of discontinuity orientation, validated by the user's expertise. Traditional stereographic approaches in kinematic analysis are limited in characterizing spatially variable slopes and discontinuity networks, providing only generic estimations of unstable slope surfaces and discontinuity intersections. This limitation is overcome by new algorithms that incorporate 3D spatial relationships of both the discontinuity network and the rock slope, resulting in a more accurate and site-specific kinematic characterization of the rock cliff, emphasized by 3D visualizations of the critical planes and potential rock failure volumes. The new approach significantly impacts the time, effort, and cost of the engineering projects, allowing to quantitatively define the potential unstable areas with a fast analysis. The Authors stress the importance of integrating digital workflows with comprehensive field-based characterizations of the local litho-stratigraphic and structural setting to effectively utilize large datasets and partially automated procedures.

Machine learning-based rapid analysis of the failure progress of thin-film electrodes from electric impedance spectroscopy (EIS) data

Abstract Parylene C is a common substrate and encapsulation material used in implantable microelectrodes. Its reliability and failure are of great significance in the research and application of microelectrodes. In this study, three different failure stages of Parylene C thin-film electrodes were modeled using equivalent circuits, and the electric impedance spectroscopy of the electrodes were rapidly analyzed 9 different machine learning algorithms to identify the failure stages. The results showed that the three equivalent circuit models can represent the dynamics of the three failure stages of the Parylene C thin-film electrodes. The support vector machine (SVM) algorithm achieves more than 93% accuracy in identifying the equivalent circuit models from electric impedance spectroscopy data with an average time of 0.0273s. The SVM algorithm has great potential in fast analysis of electric impedance spectroscopy for the endurability study and application of implantable microelectrodes.

Seismic Isolators Layout Optimization Using Genetic Algorithm Within the Pymoo Framework

In most previous studies, seismic base isolation system optimization has mainly focused on determining isolation layer parameters. However, the subsequent steps of isolator device selection and positioning can significantly impact overall system performance. To address these shortcomings, we propose an alternative optimization approach demonstrated through two models: regular and irregular 8-storey reinforced concrete structures. This approach utilizes the Pymoo framework and commercially available isolators to find optimal isolator layout configurations in two steps. First, using the equivalent lateral force (ELF) procedure, an initial population of seismic isolators meeting shear strain, base shear coefficient, and buckling requirements was randomly selected from suppliers' elastomeric bearing catalogs. Second, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used to improve the seismic response of the models under the fast nonlinear analysis (FNA) method by minimizing peak roof acceleration, inter-story drift ratio, displacement of the isolated base layer, as well as maximizing the fundamental period. The results underscore the effectiveness of this approach in improving seismic response. Compared to fixed-base structures, the optimal solutions achieved more than double the fundamental period, reduced peak roof acceleration by over 70%, and diminished base shear force by approximately 50%. This methodology can serve as a reference for future research across various structure types, including hybrid isolation systems and steel structures. Doi: 10.28991/CEJ-2024-010-08-07 Full Text: PDF

Rapid determination of nine preservatives in tobacco flavor by three phase-hollow fiber-liquid phase microextraction-high performance liquid chromatography

Tobacco flavors are extensively utilized in traditional tobacco products, electronic nicotine, heated tobacco products, and snuff. To inhibit fungal growth arising from high moisture content, preservatives such as benzoic acid (BA), sorbic acid (SA), and parabens are often incorporated into tobacco flavors. Nonetheless, consuming preservatives beyond safety thresholds may pose health risks. Therefore, analytical determination of these preservatives is crucial for both quality assurance and consumer protection. For example, BA and SA can induce adverse reactions in susceptible individuals, including asthma, urticaria, metabolic acidosis, and convulsions. Parabens, because of their endocrine activity, are classified as endocrine-disrupting chemicals. Despite extensive research, the concurrent quantification of trace-level hydrophilic (BA and SA) and hydrophobic (methylparaben, ethylparaben, isopropylparaben, propylparaben, butylparaben, isobutylparaben, and benzylparaben) preservatives in tobacco flavors remains challenging. Traditional liquid phase extraction coupled with high performance liquid chromatography (HPLC) often results in high false positive rates and inadequate sensitivity. In contrast, tandem mass spectrometry offers high sensitivity and specificity; however, its widespread application is limited by laborious sample preparation and significant operational costs. Therefore, it is crucial to establish a fast and sensitive sample pretreatment and analysis method for the nine preservatives in tobacco flavors. In this study, a method for the simultaneous determination of the nine preservatives (SA, BA and seven parabens) in tobacco flavor was established based on three phase-hollow fiber-liquid phase microextraction (3P-HF-LPME) technology combined with HPLC. To obtain the optimal pretreatment conditions, extraction solvent type, sample phase pH, acceptor phase pH, sample phase volume, extraction time, and mass fraction of sodium chloride, were examined. Additionally, the HPLC parameters, including UV detection wavelength and mobile phase composition, were refined. The optimal extraction conditions were as follows: dihexyl ether was used as extraction solvent, 15 mL sample solution (pH 4) was used as sample phase, sodium hydroxide aqueous solution (pH 12) was used as acceptor phase, and the extraction was carried out at 800 r/min for 30 min. Chromatographic separation was accomplished using an Agilent Poroshell 120 EC-C18 column (100 mm×3 mm, 2.7 μm) and a mobile phase comprising methanol, 0.02 mol/L ammonium acetate aqueous solution (containing 0.5% acetic acid), and acetonitrile for gradient elution. Under the optimized conditions, the nine target analytes showed good linear relationships in their respective linear ranges, the correlation coefficients (r) were ≥0.9967, limits of detection (LODs) and quantification (LOQs) were 0.02-0.07 mg/kg and 0.08-0.24 mg/kg, respectively. Under two spiked levels, the enrichment factors (EFs) and extraction recoveries (ERs) of the nine target analytes were 30.6-91.1 and 6.1%-18.2%, respectively. The recoveries of the nine target analytes ranged from 82.2% to 115.7% and the relative standard deviations (RSDs) (n=5) were less than 14.5% at low, medium and high levels. The developed method is straightforward, precise, sensitive, and well-suited for the rapid screening of preservatives in tobacco flavor samples.

Fast Discriminant Analysis With Adaptive Reconstruction Structure Preserving.

Neighborhood reconstruction methods have been widely applied to feature engineering. Existing reconstruction-based discriminant analysis methods normally project high-dimensional data into a low-dimensional space while preserving the reconstruction relationships among samples. However, there are three limitations: 1) the reconstruction coefficients are learned based on the collaborative representation of all sample pairs, which requires the training time to be the cube of the number of samples; 2) these coefficients are learned in the original space, ignoring the interference of the noise and redundant features; and 3) there is a reconstruction relationship between heterogeneous samples; this will enlarge the similarity of heterogeneous samples in the subspace. In this article, we propose a fast and adaptive discriminant neighborhood projection model to tackle the above drawbacks. First, the local manifold structure is captured by bipartite graphs in which each sample is reconstructed by anchor points derived from the same class as that sample; this can avoid the reconstruction between heterogeneous samples. Second, the number of anchor points is far less than the number of samples; this strategy can reduce the time complexity substantially. Third, anchor points and reconstruction coefficients of bipartite graphs are updated adaptively in the process of dimensionality reduction, which can enhance the quality of bipartite graphs and extract discriminative features simultaneously. An iterative algorithm is designed to solve this model. Extensive results on toy data and benchmark datasets show the effectiveness and superiority of our model.

Characterization of loading, relaxation, and recovery behaviors of high‐density polyethylene using a three‐branch spring‐dashpot model

AbstractThis paper presents an analysis of the stress evolution of high‐density polyethylene (HDPE) at loading, relaxation, and recovery stages in a multi‐relaxation‐recovery (RR) test. The analysis is based on a three‐branch spring‐dashpot model that uses the Eyring's law to govern the viscous behavior. The spring‐dashpot model comprises two viscous branches to represent the short‐ and long‐term time‐dependent stress responses to deformation, and a quasi‐static branch to represent the time‐independent stress response. A fast numerical analysis framework based on genetic algorithms was developed to determine values for the model parameters so that the difference between the simulation and the experimental data could be less than 0.08 MPa. Using this approach, values of the model parameters were determined as functions of deformation and time so that the model can simulate the stress response at loading, relaxation, and recovery stages of the RR test. The simulation also generated 10 sets of model parameter values to examine their consistency. The study concludes that the three‐branch model can serve as a suitable tool for analyzing the mechanical properties of HDPE, and values for the model parameters can potentially be used to characterize the difference among PEs for their mechanical performance.Highlights Developed computer programs to determine parameter values automatically. Explained the unusual stress drop during stress recovery after unloading. Evaluated the statistical range of the parameter values for the good fitting.

Reliability analysis of deep tunnels in spatially varying brittle rocks using interval and random field modelling

Rock properties are estimated using objective lab/in-situ testing and subjective judgements invoking different types of uncertainties, i.e., aleatory, and epistemic, along them, often indicated by varying information levels. This study presents a unified reliability method to integrate the spatial variability of inputs modelled via alternate uncertainty models (intervals and probability-boxes (p-boxes)) with those modelled as stochastic variables via probability distributions. The methodology employs advanced Karhunen–Loève decomposition to generate interval and random fields of inputs modelled via alternate and stochastic models, respectively. Input properties are allocated to the zones of the numerical model in Fast Lagrangian Analysis of Continua-2D (FLAC-2D) based on their spatial dependency and correlation functions through a developed MATLAB-FLAC coupled code. The methodology is demonstrated for a deep tunnel in Canada to be constructed along a massive rock prone to brittle failures. Intact rock properties are modelled as stochastic variables due to objective estimation, while Geological Strength Index (GSI) and deformation modulus are modelled using alternate models (interval and p-box, respectively) due to subjective and hybrid estimation (double uncertainty propagation algorithm). The results of the methodology are compared with those of traditional deterministic and random field methods. The methodology reduces the subjectivity invoked by including unavailable additional information (e.g., assuming probability distributions of inputs based on literature) and propagates the originally available information of inputs accurately. The final outputs are the p-boxes of response parameters (i.e., displacements and damage zone) instead of their fixed values (deterministic analysis) and probability distributions (traditional reliability analysis), indicating the propagation of both impreciseness and variability of inputs by the method. For this case study, the p-boxes of outputs were bounding their values/distributions estimated via traditional analyses, verifying the accuracy of the methodology. Further, the impreciseness in the outputs, highest in the damage zone extent, was due to imprecision in the estimated GSI.

Fast Food Industry Strategy Analysis: Evidence from the Yum! Brands

Yum! Brands (YB) is a world-famous fast food chain group, and its business strategy and model are typical in the industry. With the deepening of digital transformation, it brings both opportunities and challenges to the fast food industry. Specific analysis of YB's external environment, internal environment and financial statements during this article. The analysis shows that YB has advantages in terms of market position, supply chain management and brand awareness, but its disadvantages are over-reliance on the franchise model and single market. Meanwhile, it also faces external opportunities and threats such as digital transformation and economic fluctuations. To improve the level of YB's strategic planning, this paper proposes targeted strategic choices and implementation schemes based on the analysis results, including exploiting health products and increasing investment in emerging markets, etc. This paper aims to help YB consolidate its position and develop in the fierce market competition. It also provides a reference for the overall strategic planning of the future fast food industry.

Rapid prediction of the chemical composition of pet food using a benchtop and handheld near-infrared spectrometer

Quality of pet foods can be affected by many factors such as raw materials, formulations, and processing techniques. The pet food manufacturers require fast analyses to control the nutritional quality of their products. Herein, near-infrared spectroscopy (NIR) was evaluated to quantify the chemical composition of pet food, and the performances of two NIR spectrometers were investigated and compared: a benchtop instrument (1000–2500 nm) and a low-cost handheld instrument (900–1700 nm). Seventy cat food and thirty-six dog samples were characterized using reference methods for crude protein, crude fat, crude fibre, crude ash, moisture, calcium (Ca), and phosphorus (P). Principal component regression (PCR) and partial least squares regression (PLSR) were used to establish the models that involved the cat food and mixed model. The characteristic wavelengths were selected using a competitive adaptive reweighted-sampling (CARS) algorithm. The Optimal models obtained from the benchtop instrument for crude protein, crude fat, and moisture were classified as “Good” or “Very good” (Residual prediction variation (RPD) > 3), for crude fibre were classified as “Poor” (RPD>2), and for crude ash, Ca and P (RPD<2) were classified as “Very poor”. The Optimal calibrations obtained from the handheld instrument for crude protein, crude fat, and moisture were classified as “Good” or “Very good” (RPD>3), for crude fibre, crude ash, Ca, and P were classified as “Very poor” (RPD<2). Generally, the the performance of benchtop and handheld instrument was close, and the cat food model outperformed the mixed model. Results from the current study revealed the potential to monitor the chemical compositions in pet food on a large scale.

Artificial intelligence and electrocardiography: A modern approach to heart rate monitoring

The integration of Artificial Intelligence (AI) in Electrocardiography (ECG) and Photoplethysmography (PPG) signifies AI's profound influence on heart rate monitoring and analysis. ECG traditionally offers critical insights into cardiac health, necessitating expert interpretation. This study introduces an AI framework with Fast Fourier Transformation Analysis for swift, human-like interpretation of complex ECG signals. A multilayer AI Network accurately detects intricate features, enhancing ECG analysis precision. Leveraging comprehensive datasets, AI models proficiently identify heart dysfunctions like atrial fibrillation and hypertrophic cardiomyopathy, and can estimate age, sex, and race. The proliferation of mobile ECG technologies has spurred AI-based ECG phenotyping, impacting clinical and population health. This research explores AI's role in enhancing cardiac health assessment and clinical decision-making using MATLAB, acknowledging its transformative potential and inherent limitations.

Coupled vibration of low-medium-speed maglev vehicle-guideway system on isolated bridge with lead rubber bearings

This paper investigates the dynamic response of low-medium-speed (LMS) maglev vehicle moving on the isolated bridge with lead rubber bearings (LRBs). In the vehicle-guideway bridge model, the vehicle is simulated as a 50-degree-of-freedom model consisting of a car-body and ten bogie modules. The guideway bridge with LRB is established by the finite element method, and the guideway is interacted with the vehicle by the actively controlled electromagnetic forces. The LRB is simulated by the nonlinear spring element for reflecting the hysteretic performance, and a fast nonlinear analysis (FNA) method is proposed to achieve the potential nonlinear behavior of LRB under vehicle load. Then, the dynamic response of maglev vehicle running on the isolated bridge with LRB is investigated and compared to that on the non-isolated bridge. The effect of vehicle speed and LRB isolation degree on the coupled system responses is studied. Furthermore, the driving quality of vehicle on LRB bridge is discussed, and the applicability of LRB to maglev line bridge is thoroughly evaluated. The results show that the installation of LRB exhibits relatively insignificant influence on the vertical response of vehicle-guideway system, while could enlarge the lateral response. The lateral response of coupled system is much more vulnerable to the isolation degree, and it is recommended that the isolation degree of LRB should not exceed 2.50 to guarantee the driving comfort and running safety.

A computational workflow for analysis of missense mutations in precision oncology

Every year, more than 19 million cancer cases are diagnosed, and this number continues to increase annually. Since standard treatment options have varying success rates for different types of cancer, understanding the biology of an individual's tumour becomes crucial, especially for cases that are difficult to treat. Personalised high-throughput profiling, using next-generation sequencing, allows for a comprehensive examination of biopsy specimens. Furthermore, the widespread use of this technology has generated a wealth of information on cancer-specific gene alterations. However, there exists a significant gap between identified alterations and their proven impact on protein function. Here, we present a bioinformatics pipeline that enables fast analysis of a missense mutation’s effect on stability and function in known oncogenic proteins. This pipeline is coupled with a predictor that summarises the outputs of different tools used throughout the pipeline, providing a single probability score, achieving a balanced accuracy above 86%. The pipeline incorporates a virtual screening method to suggest potential FDA/EMA-approved drugs to be considered for treatment. We showcase three case studies to demonstrate the timely utility of this pipeline. To facilitate access and analysis of cancer-related mutations, we have packaged the pipeline as a web server, which is freely available at https://loschmidt.chemi.muni.cz/predictonco/.Scientific contributionThis work presents a novel bioinformatics pipeline that integrates multiple computational tools to predict the effects of missense mutations on proteins of oncological interest. The pipeline uniquely combines fast protein modelling, stability prediction, and evolutionary analysis with virtual drug screening, while offering actionable insights for precision oncology. This comprehensive approach surpasses existing tools by automating the interpretation of mutations and suggesting potential treatments, thereby striving to bridge the gap between sequencing data and clinical application.

Effects of air gap eccentricity on different rotor structures for PMSM in electric vehicles

In addition to research related to static eccentricity and dynamic eccentricity examining fault and diagnostic methods in various types of motors, there are also studies conducted for motors with non-uniform air gaps similar to the condition. In these studies, improvements and changes in motor performance for such types of motors can be observed. In this study, the effects of the rotor structures determined in the study and the air gap shape, which is effective by changing the eccentricity ratio, on the motor performance of the interior permanent magnet synchronous motor designed for electric vehicle applications are examined. Besides the elliptical rotor structure, which is widely researched in dynamic eccentricity studies, these eccentric motor structures also include novel rectangular and polygonal eccentric rotor structures. Motor models with non-uniform air gap structures are designed in two dimensions and subjected to simulation and analysis studies using the finite element method. At the end of the study, along with the parameters identified for motor performance, the harmonic components of the stator current, radial air gap flux density and the output torque obtained through Fast Fourier Transform analysis have been provided.

Deep learning-based prediction of 3-dimensional silver contact shapes enabling improved quality control in solar cell metallization

The industrial metallization of Si solar cells predominantly relies on screen printing, with silver as the preferred electrode material. However, the design of commercial screens often leads to suboptimal silver usage and increased electrical resistance due to print-related inhomogeneities like mesh marks, constrictions and spreading. Real-time monitoring of quality parameters during production has thus become increasingly critical. Current inline optical quality control systems usually only include 2D visualizations of the printed layout, which limits their effectiveness in quality control. Options that allow 3D measurements are usually slow, expensive, and therefore not worth considering in most cases. This research focuses on the development of a model that can estimate the three-dimensional shape of printed contact fingers from a single 2D image without the need of additional hardware using deep learning. Furthermore, a workflow for the generation of training data, which involves the creation of image pairs from a 2D microscope and a 3D confocal laser scanning microscope (CLSM) to accurately represent solar cell fingers, is presented. After model training, the predicted height maps are compared with the ground truth height maps, and the robustness of the model with respect to a paste variation and screen parameter variation is examined. The results confirm the feasibility and reliability of deep learning-based 3D shape estimation, extending its applicability to new, previously unseen data from screen-printed contact fingers. With a structural similarity index (SSIM) score of 0.76, a strong correlation between the estimated and ground truth height maps is established. In summary, our deep learning-based approach for height map estimation offers an effective and reliable solution for fast inline detection and analysis of the cross-sectional area of the printed contact fingers.

A comprehensive and innovative chemometric approach: Archaeometric analysis of the sherds from the Neolithic Period to the Chalcolithic and early Bronze Age with the full deployment of FTIR's molecular spectroscopic capabilities

In this study, the mineralogical composition of 284 sherds obtained from the sites of Yeşilova Höyük and Yassıtepe Höyük, two of the oldest settlements in Western Anatolia, and corresponding to a wide period starting from the Neolithic period to the Chalcolithic and Early Bronze Age (EBA), was studied by attenuated total reflectance (ATR) Fourier transform infrared (FTIR) spectroscopy. FTIR data was used to classify the sherds and state their firing conditions as a result of determining their mineralogical composition using principal component analysis (PCA), multivariate curve resolution (MCR), and hierarchical cluster analysis (HCA). According to the PCA results, the second-order derivative FTIR spectrum with 19 smoothing points in the range of 1300–400 cm−1 was determined to be the most successful model in classifying sherds. As a result of MCR, which allows comparison of standard minerals obtained within the scope of the study, it was determined that kaolinite, illite, chlorite, and some feldspar types were dominant in the sherds. According to HCA analysis, all sherds except five of them exhibited a similar mineralogical structure. According to the derivative spectra, it was seen that the sherds had a composition consisting of kaolinite, illite, hematite, chlorite, and some feldspar types at different rates. Upon examining the data, it becomes evident that the sherds are composed of nearly identical minerals and might have been fired in an oxidizing atmosphere. Even while 279 sherds had many mineralogical traits, N-061, EB-030, N-043, C-068, and N-059 were found to have distinct proportions of comparable minerals, including more kaolinite, illite, calcite, and chlorite. Regarding color, additives, and the amount of ceramic pastes present, these five sherds differ from the others. This suggests that there might have been attempts at manufacture or pots brought in from outside the Yeşilova Höyük-Yassıtepe Höyük sites. The results provided from the derivative spectra showed that all sherds except five of them have been fired at about at temperatures slightly above 450–500 °C due to their relatively low kaolinite character, but below 800 °C because diopside and similar high-temperature minerals could not be detected. The five sherds may have been fired at a temperature of around 450–500 °C due to the presence of a high character of kaolinite, illite, and chlorite. It was understood that the residents of the sites of Yeşilova Höyük and Yassıtepe Höyük produced their ceramics using the same raw materials and the same production methods from the Neolithic period to EBA thanks to FTIR and chemometrics, which are very good tools for analyzing large numbers of sherds with low cost and fast analysis time.

Intelligent surrogate model of a high-temperature superconducting cable for reliable energy delivery: short-circuit fault performance

High-temperature superconducting (HTS) cables are promising solutions for electric power transmission of renewable energy resources, where their fault performance study is vital to avoid power interruptions in the grid. In this study, a fast intelligent surrogate model was presented to estimate the fault performance of a 22.9 kV/50 MW HTS cable to make fast fault performance analysis of the HTS cables possible during the design stage. Different fault scenarios were considered under different fault durations, fault resistances, and types of faults. Then, the fault energy, fault current, fault type, fault duration, and fault resistance were fed into the surrogate model as inputs. The outputs were the temperature of the rare-earth barium copper oxide (ReBCO) tapes, the former temperature, the ReBCO layer current, and the total resistance of each phase. For surrogate modelling, cascade forward neural networks (CFNNs) were used. The results show that the CFNN-based model estimated the fault performance of the cable with an average accuracy of 99.1%. Finally, the impact of considering fault energy, fault current, and both, as the inputs of the models, on the final accuracy were explored. The results show that by considering the fault energy, the accuracy of the surrogate model can be increased.

Fast in-line failure analysis of sub-micron-sized cracks in 3D interconnect technologies utilizing acoustic interferometry

More than Moore technology is driving semiconductor devices towards higher complexity and further miniaturization. Device miniaturization strongly impacts failure analysis (FA), since it triggers the need for non-destructive approaches with high resolution in combination with cost and time efficient execution. Conventional scanning acoustic microscopy (SAM) is an indispensable tool for failure analysis in the semiconductor industry, however resolution and penetration capabilities are strongly limited by the transducer frequency. In this work, we conduct an acoustic interferometry approach, based on a SAM-setup utilizing 100 MHz lenses and enabling not only sufficient penetration depth but also high resolution for efficient in-line FA of Through Silicon Vias (TSVs). Accompanied elastodynamic finite integration technique-based simulations, provide an in-depth understanding concerning the acoustic wave excitation and propagation. We show that the controlled excitation of surface acoustic waves extends the contingency towards the detection of nm-sized cracks, an essential accomplishment for modern FA of 3D-integration technologies.

Artificial Intelligence‐Driven Smart Scan: A Rapid, Automatic Approach for Comprehensive Imaging and Spectroscopy for Fast Compositional Analysis

Nanomaterial properties and functionalities are influenced by their shape, size, and chemical composition. The importance of these parameters highlights the need for a statistically robust analysis of a large particle population, necessitating automation. This study introduces a neural network‐empowered smart scan technique that achieves a relative increase in speed compared to traditional energy‐dispersive X‐ray spectroscopy (EDX) mapping. The main advantage is that it reduces the required dose, decreasing potential damage to the sample by avoiding unnecessary exposure. It holds potential use in other multimodal scanning transmission electron microscopy or scanning‐based imaging approaches. In the first example, identifying particles in a matrix with a trained neural network reduces the acquisition time by two orders of magnitude. This acceleration enables a statistical compositional analysis of thousands of particles in less than 1 h. Similar improvements are observed for atomic resolution. The discrete positions of atoms identified by the trained network allow for selective EDX sampling at these centers, thereby identifying the atomic species of the column with much‐reduced sampling. Consequently, a lower sampling dose is required, enabling mapping of more delicate materials with high lateral resolution and at a high statistical confidence interval. Even though manual training is still required, this approach greatly benefits repetitive quality control tasks.

Mechanism-driven improved SVMD: an indirect approach for rail corrugation detection using axle box acceleration

Abstract Addressing the challenge of the current inability to qualitatively identify rail corrugation damage accurately from axle box acceleration data, this study proposes a novel approach. To indirectly identify rail corrugation from axle box acceleration, we introduce an improved successive variational mode decomposition (SVMD) algorithm, coupled with a deep learning model for corrugation recognition. First, a numerical model of the vehicle-rail-track slab system is established, considering rail corrugation. Indicator analysis is integrated into the SVMD. The improved SVMD is employed to decompose and reconstruct axle box acceleration, achieving noise reduction and extraction of useful components. Next, we apply the fast Continuous Wavelet Transform analysis method to effectively transform one-dimensional data into two-dimensional images. Finally, the You Only Look Once (YOLO) model serves as a classifier for the classification and recognition of corrugation with different wavelengths. The results demonstrate that the mechanism-driven improved SVMD effectively extracts corrugation components from axle box acceleration, while the YOLO model achieves rapid and efficient identification and classification of corrugation with different wavelengths. The results show that compared with other traditional models, the training time of the YOLO model is 60%–90% of the training time of the traditional algorithm, and the Recall rate is 1.15–1.45 times that of the traditional algorithm. In terms of wavelength identification, the YOLO model has a recognition rate of 98% for different wavelengths. The proposed approach offers an innovative and efficient solution for identifying rail corrugation damage in axle box acceleration data.

LN: A Flexible Algorithmic Framework for Layered Queueing Network Analysis

Layered queueing networks (LQNs) are an extension of ordinary queueing networks useful to model simultaneous resource possession and stochastic call graphs in distributed systems. Existing computational algorithms for LQNs have primarily focused on mean-value analysis. However, other solution paradigms, such as normalizing constant analysis and mean-field approximation, can improve the computation of LQN mean and transient performance metrics, state probabilities, and response time distributions. Motivated by this observation, we propose the first LQN meta-solver, called LN, that allows for the dynamic selection of the performance analysis paradigm to be iteratively applied to the submodels arising from layer decomposition. We report experiments where this added flexibility helps us to reduce the LQN solution errors. We also demonstrate that the meta-solver approach eases the integration of LQNs with other formalisms, such as caching models, enabling the analysis of more general classes of layered stochastic networks. Additionally, to support the accurate evaluation of the LQN submodels, we develop novel algorithms for homogeneous queueing networks consisting of an infinite server node and a set of identical queueing stations. In particular, we propose an exact method of moment algorithms, integration techniques for normalizing constants, and a fast non-iterative mean-value analysis technique.