Neighboring Components Research Articles

Superoxide radical-induced rapid valence cycling of Mn(II/III/IV) for efficient degradation of organic pollutants

Transition metal oxides are one of the commonly used catalysts for ozone oxidation, and the slow redox cycling of different valence metal components inside the catalysts currently becomes a key bottleneck limiting their catalytic activity. In this work, the superoxide radicals generated at the N/C sites were used to reduce the neighboring high-valence metal components to low-valence states to construct a new mechanism for the rapid cyclic transition of Mn (II/III/IV) valence states. What's more, N/C sites enhanced the ozone adsorption and conversion ability, synergized with the adjacent MnO2 sites to generate interfacial hydroxyl radicals and superoxide radicals, and effectively inhibited the quenching effect of inorganic salt ions (e.g., Cl-, SO42-) on the free radicals. For the actual printing and dyeing wastewater, the COD (chemical oxygen demand) removal rate was as high as 70 % in 60 min and remained stable in 10 cycles with almost no manganese ions leaching. In addition, characterization such as EPR combined with DFT calculations showed that the dual-site structure significantly increased the oxygen vacancy content and Lewis acidity, and the synergistic interaction between graphite N and MnO2 was the most favorable for ozone conversion with the lowest reaction energy barriers.

A Hybrid Neural Network‐Based Improved PSO Algorithm for Gas Turbine Emissions Prediction

AbstractIn gas‐fired power plants, emissions may reduce turbine blade rotation, thus decreasing power output. This study proposes a hybrid model integrating the Feed forward Neural Network (FFNN) model and Particle Swarm Optimization (PSO) algorithm to predict gas emissions from natural gas power plants. The FFNN predicts gas turbine nitrogen oxides (NOx) and carbon monoxide (CO) emissions, while the PSO optimizes FFNN weights, improving prediction accuracy. The PSO adopts a unique random number selection strategy, incorporating the K‐Nearest Neighbor (KNN) algorithm to reduce prediction errors. Neighbor Component Analysis (NCA) selects parameters most correlated with CO and NOx emissions. The hybrid model is constructed, trained, and testedusing publicly available datasets, evaluating performance with statistical metrics like Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Results show significant improvement in FFNN training with the PSO algorithm, boosting CO and NOx prediction accuracy by 99.18% and 82.11%, respectively. The model achieves the lowest MSE, MAE, and RMSE values for CO and NOx emissions. Overall, the hybrid model achieves high prediction accuracy, particularly with optimized PSO parameter selection using seed random generators.

An Algorithm for Measuring the Similarity of Histograms for Texture Image Segmentation

A simple algorithm for measuring the similarity between multi-column histograms is presented. The proposed algorithm is intended for texture segmentation of images using histograms as texture features. The purpose of developing such a specialized algorithm is to more accurately determine the boundaries between neighboring texture segments. The algorithm is specially designed so that to express the similarity value as a percentage. The main peculiarity of the proposed algorithm is that when calculating the similarity value, it considers not only the corresponding histogram columns but also takes into account their neighboring components. Due to this, the algorithm more adequately evaluates the similarity of histograms. The proposed algorithm was implemented as a computer program as an integral part of the image segmentation model. The efficiency of the histogram comparison algorithm is indirectly confirmed by the texture segmentation results of the image segmentation model in image processing experiments.

Serum analysis based on SERS combined with 2D convolutional neural network and Gramian angular field for breast cancer screening

Breast cancer is a significant cause of death among women worldwide. It is crucial to quickly and accurately diagnose breast cancer in order to reduce mortality rates. While traditional diagnostic techniques for medical imaging and pathology samples have been commonly used in breast cancer screening, they still have certain limitations. Surface-enhanced Raman spectroscopy (SERS) is a fast, highly sensitive and user-friendly method that is often combined with deep learning techniques like convolutional neural networks. This combination helps identify unique molecular spectral features, also known as “fingerprint”, in biological samples such as serum. Ultimately, this approach is able to accurately screen for cancer. The Gramian angular field (GAF) algorithm can convert one-dimensional (1D) time series into two-dimensional (2D) images. These images can be used for data visualization, pattern recognition and machine learning tasks. In this study, 640 serum SERS from breast cancer patients and healthy volunteers were converted into 2D spectral images by Gramian angular field (GAF) technique. These images were then used to train and test a two-dimensional convolutional neural network-GAF (2D-CNN-GAF) model for breast cancer classification. We compared the performance of the 2D-CNN-GAF model with other methods, including one-dimensional convolutional neural network (1D-CNN), support vector machine (SVM), K-nearest neighbor (KNN) and principal component analysis-linear discriminant analysis (PCA-LDA), using various evaluation metrics such as accuracy, precision, sensitivity, F1-score, receiver operating characteristic (ROC) curve and area under curve (AUC) value. The results showed that the 2D-CNN model outperformed the traditional models, achieving an AUC value of 0.9884, an accuracy of 98.13%, sensitivity of 98.65% and specificity of 97.67% for breast cancer classification. In this study, we used conventional nano-silver sol as the SERS-enhanced substrate and a portable laser Raman spectrometer to obtain the serum SERS data. The 2D-CNN-GAF model demonstrated accurate and automatic classification of breast cancer patients and healthy volunteers. The method does not require augmentation and preprocessing of spectral data, simplifying the processing steps of spectral data. This method has great potential for accurate breast cancer screening and also provides a useful reference in more types of cancer classification and automatic screening.

Rapid and non-invasive detection of cystic echinococcosis in sheep based on serum fluorescence spectrum combined with machine learning algorithms.

Cystic echinococcosis (CE) is a grievous zoonotic parasitic disease. Currently, the traditional technology of screening CE is laborious and expensive, developing an innovative technology is urgent. In this study, we combined serum fluorescence spectroscopy with machine learning algorithms to develop an innovative screening technique to diagnose CE in sheep. Serum fluorescence spectra of Echinococcus granulosus sensu stricto-infected group (n = 63) and uninfected E. granulosus s.s. group (n = 60) under excitation at 405 nm were recorded. The linear support vector machine (Linear SVM), Quadratic SVM, medium radial basis function (RBF) SVM, K-nearest neighbor (KNN), and principal component analysis-linear discriminant analysis (PCA-LDA) were used to analyze the spectra data. The results showed that Quadratic SVM had the great classification capacity, its sensitivity, specificity, and accuracy were 85.0%, 93.8%, and 88.9%, respectively. In short, serum fluorescence spectroscopy combined with Quadratic SVM algorithm has great potential in the innovative diagnosis of CE in sheep.

Digital Technology Drives Changes in the Communication Order of Hunan Sports and Culture

Abstract In the rapid development of digital technology, it has become urgent to explore the order change and development strategy of sports culture communication. This study extracts and analyzes the factors affecting the order change of sports culture communication through partial least squares regression and nearest neighbor component analysis. It was found that between 2016 and 2023, the communication channels, contents and audience order of sports culture in Hunan changed significantly, in which the traditional media communication accounted for 82.52% in 2016, while 81% of the residents accessed sports information through WeChat (26.22%), microblogs (27.73%), and computer webpages (26.78%) in 2023. In 2018 and 2019, the proportion of sportsmanship culture in the overall sports culture communication content is 40.77% and 38.87%, respectively. The development suggestion model in this paper provides an essential reference for the future order change of sports culture communication in Hunan region.

Nondestructive Estimation of Neutral Temperature in Rails: A Comparative Study of Machine Learning Strategies

This paper presents the latest findings of a nondestructive evaluation technique currently under development at the University of Pittsburgh to determine the rail neutral temperature (RNT) in continuous welded rails. The technique is based on the extraction of relevant features from rail vibrations and the use of machine learning (ML) to associate these features to the longitudinal stress of the rail of interest. The features contain the spectral information of the vibrations and are pooled together by frequency domain decomposition for input to ML algorithms. Minimum redundancy–maximum relevance and neighboring component analysis are used to identify relevant features to reduce the size of the input vector. In addition, seven algorithms were considered to identify the most accurate model for neutral temperature with respect to the ground truth RNT measured with a strain-gage rosette. The data used in this study were collected from a 5° curved rail on concrete ties. The vibrations were triggered with a hammer and recorded with a few wireless and wired accelerometers attached on the railhead. The results showed that the Gaussian process regressor performs best, and as few as 20 frequencies can be used to predict the RNT with sufficient accuracy.

A Numerical Approach for Predicting Temperature Distribution, Heat Flow, and Material Deformation of Printed Circuit Board during Laser Soldering

This paper presents a numerical simulation approach using the finite element method (FEM) in ANSYS software to overcome the limited accessibility of laser soldering machines which are expensive. The study aims to examine the viability of simulating a laser soldering process using ANSYS steady-state thermal simulation. The simulation aims to produce accurate predictions for temperature distribution, heat flow, and deformation of the material during the process. A Gaussian laser beam equation is used to analyse the interaction between the laser beam and metallic components, particularly on PCB boards. This paper evaluates the properties of a laser beam and the Sn-3.0Ag-0.5Cu (SAC305) lead-free solders which are commonly used in electronic industries. As a result of the thermal analysis, the temperature distribution within the solder spots, as well as the copper as printed circuit board (PCB) can be determined, whereas the mechanical analysis of the materials can be determined by looking at their deformation and stress in the system. The results show that the laser beam effectively contains heat within the solder contacts, reducing the risk of excessive heat transfer. The copper acts as an efficient heat sink, managing thermal energy during the process. However, the solder point experiences substantial deformation and stress, indicating potential limitations in its performance. Nonetheless, the neighbouring components and solder joints exhibit a factor of safety, ensuring the overall safety and reliability of the system.

Analysis and Evaluation of Feature Selection and Feature Extraction Methods

Hand gestures are widely used in human-to-human and human-to-machine communication. Therefore, hand gesture recognition is a topic of great interest. Hand gesture recognition is closely related to pattern recognition, where overfitting can occur when there are many predictors relative to the size of the training set. Therefore, it is necessary to reduce the dimensionality of the feature vectors through feature selection techniques. In addition, the need for portability in hand gesture recognition systems limits the use of deep learning algorithms. In this sense, a study of feature selection and extraction methods is proposed for the use of traditional machine learning algorithms. The feature selection methods analyzed are: maximum relevance and minimum redundancy (MRMR), Sequential, neighbor component analysis without parameters (NCAsp), neighbor component analysis with parameters (NCAp), Relief-F, and decision tree (DT). We also analyze the behavior of feature selection methods using classification and recognition accuracy and processing time. Feature selection methods were fed through seventeen feature extraction functions, which return a score proportional to its importance. The functions are then ranked according to their scores and fed to machine learning algorithms such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (DT). This work demonstrates that all feature selection methods evaluated on ANN provide better accuracy. In addition, the combination and number of feature extraction functions influence the accuracy and processing time.

The construction of S-scheme heterostructure in ultrathin WS2/Zn3In2S6 nanosheets for enhanced photocatalytic hydrogen evolution

Metal sulfide based photocatalysts are considered to be economic, environmentally benign and renewable. The rapid recombination of photogenerated electrons and holes and low solar energy utilization efficiency, however, remain a huge bottleneck. Herein, two-dimensional/two-dimensional (2D/2D) S-scheme WS2/Zn3In2S6 heterostructure with ultrathin nanosheets intervening between neighboring component has been designed. The large and intimate S-scheme heterojunctions facilitate interfacial charge separation/transfer and optimize the available redox potential. Besides, the ultrathin 2D/2D heterostructure ensures large specific surface area, maximized interface synergistic interaction, and effective exposure of surface active sites. As a result, 2 wt% WS2/Zn3In2S6 exhibits a high photocatalytic hydrogen production rate of 30.21 mmol·g−1·h−1 under simulated solar light illumination with an apparent quantum efficiency of 56.1% at 370 nm monochromatic light, far exceeding pristine Zn3In2S6 (6.65 mmol·g−1·h−1). Our work underscores the significance of integrating morphology engineering and S-scheme heterojunctions design for high-efficient and low-cost photocatalysts.

Automated Detection and Characterization of Cracks on Concrete Using Laser Scanning

Accurate crack detection and characterization on concrete are essential for the maintenance, safety, and serviceability of various infrastructures. In this paper, an innovative approach was developed to automatically measure the cracks from 3D point clouds collected by a phase-shift terrestrial laser scanner (TLS) (FARO Focus3D S120). The approach integrates several techniques to characterize the cracks, which include the deviation on point normal determined using k-nearest neighbor (kNN) and principal components analysis (PCA) algorithms to identify the cracks, and principal axes and curve skeletons of cracks to determine the projected and real dimensions of cracks, respectively. The coordinate transformation was then performed to estimate the projected dimensions of cracks. Curve skeletons and cross sections of cracks were extracted to represent the real dimensions. Two cases of surface cracks were used to validate the developed approach. Because of the differences in definitions of the crack dimension in the three methods and due to the curve shape of the crack, the width and depth of cracks obtained from the cross-section method and manual measurement were close but slightly smaller than those measured by the projection algorithm; whereas the length of cracks determined by the curve-skeletons method was slightly larger than those obtained by the manual measurement and projection method. The real dimension of a crack has good agreements with real situations when compared with the results of the manual measurement and projection method.

Reconstruct lower‐dimensional crack paths from phase‐field point cloud

AbstractBecause of the smeared representation of phase‐field fracture, reconstructing lower‐dimensional crack paths is challenging, which may impede the further applications of the phase‐field method in the modeling of fault friction and fluid flow in fracture. In the present work, we propose to capture the crack curves or surfaces from two‐dimensional or three‐dimensional phase‐field point cloud using an optimized ridge regression algorithm, and k‐nearest neighbor and principal component analysis are used to estimate the normal direction of each segment on the identified discrete crack path. The sensitivity and computational efficiency of the proposed method are investigated thoroughly. Subsequently, this method is extended to reconstruct the complex discrete fracture networks. Finally, several benchmarks for fracture, fault friction and fluid flow problems are presented to demonstrate the strength of the proposed approach.

Degradation modelling and lifetime assessment for boiler waterwall with incomplete inspection data

Thermal fatigue cracking is a common problem for boiler waterwall tubes in power plants, resulting the risk of unscheduled plant shutdown. Developing an effective degradation model to predict the future condition of waterwall tubes is vital to assess boiler remaining life. Even though many numerical studies are available in the literature for understanding the physical mechanism (thermal fatigue cracking), the development of predictive models for future risks that can inform decision making are limited. One of the key impediments for developing predictive models is the lack of data. In the case of boiler critical subsystems, very few failures (if any) are recorded in its history. In addition, the number of inspections is limited due to access difficulties. Thus, in many practical cases, the condition data available for degradation modelling is sparse with both infrequent and (spatially) incomplete inspections. This paper presents the development of a stochastic degradation model prediction of thermal fatigue cracking severity in a boiler waterwall when the data is limited. The time evolution of the condition indicator for each waterwall tube is modelled using Markov Models and Bayesian identification algorithms are used to tractably address the large amount of missing data. The methodology presented in this paper employs a Markov Chain Monte Carlo (MCMC) scheme to account for the parameter uncertainty in the presence of missing data. Moreover, this paper also incorporates a novel grouping strategy in which the neighbouring components are grouped to tackle the problem of (spatial) data sparsity. The degradation modelling and prediction tools developed in this paper will support renewal decision making by using available onsite asset data and knowledge. A real-world case study of a boiler waterwall operating in an Australian power industry is also presented. It is found that the waterwall degradation appears to be slowing, but an additional inspection is recommended to confirm the trend.

Full-scale fatigue testing of a cast-iron wind turbine rotor shaft

Spheroidal graphite cast iron (EN-GJS), which provides a high degree of design flexibility and the possibility for lightweight design, has benefits as a material for use in structural parts in wind turbines. Comparing components made using the sand casting technique to those made using the chill casting process reveals significant potential to boost strength. However, at present, there is neither a proven design guideline nor reliable material input data for a lightweight component based on this material and fabrication process. This publication presents the results from the Gusswelleproject in chronological order. It starts with the explanation of the final setup and test plan for the full-scale rotor shaft fatigue experiment. The elaborated sensor and operational concept are then presented together with an adequate finite element method (FEM) model of the specimen and relevant neighboring components. The validation of this FEM model to ensure that the loading and the resulting local strains representing the real test bench situation is described. The usage of non-destructive testing to document the condition of the specimen from initial crack formation until integrity loss is explained followed by a comparison between the component fatigue test results and the material-based life-time forecast. A strength increase for chill-cast large components in the range of 50% is indicated. Simulation-based crack propagation studies are performed to qualitatively verify the loads responsible for the observed cracks of the component test and to further develop the possible method for crack predictions.

Combining Gaussian Process with Hybrid Optimal Feature Decision in Cuffless Blood Pressure Estimation.

Noninvasive blood pressure estimation is crucial for cardiovascular and hypertension patients. Cuffless-based blood pressure estimation has received much attention recently for continuous blood pressure monitoring. This paper proposes a new methodology that combines the Gaussian process with hybrid optimal feature decision (HOFD) in cuffless blood pressure estimation. First, we can choose one of the feature selection methods: robust neighbor component analysis (RNCA), minimum redundancy, maximum relevance (MRMR), and F-test, based on the proposed hybrid optimal feature decision. After that, a filter-based RNCA algorithm uses the training dataset to obtain weighted functions by minimizing the loss function. Next, we combine the Gaussian process (GP) algorithm as the evaluation criteria, which is used to determine the best feature subset. Hence, combining GP with HOFD leads to an effective feature selection process. The proposed combining Gaussian process with the RNCA algorithm shows that the root mean square errors (RMSEs) for the SBP (10.75 mmHg) and DBP (8.02 mmHg) are lower than those of the conventional algorithms. The experimental results represent that the proposed algorithm is very effective.

Cuffless Blood Pressure Estimation with Confidence Intervals using Hybrid Feature Selection and Decision Based on Gaussian Process

Cuffless blood pressure (BP) monitoring is crucial for patients with cardiovascular disease and hypertension. However, conventional BP monitors provide only single-point estimates without confidence intervals. Therefore, the statistical variability in the estimates is indistinguishable from the intrinsic variability caused by physiological processes. This study introduced a novel method for improving the reliability of BP and confidence intervals (CIs) estimations using a hybrid feature selection and decision method based on a Gaussian process. F-test and robust neighbor component analysis were applied as feature selection methods for obtaining a set of highly weighted features to estimate accurate BP and CIs. Akaike’s information criterion algorithm was used to select the best feature subset. The performance of the proposed algorithm was confirmed through experiments. Comparisons with conventional algorithms indicated that the proposed algorithm provided the most accurate BP and CIs estimates. To the best of the authors’ knowledge, the proposed method is currently the only one that provides highly reliable BP and CIs estimates. Therefore, the proposed algorithm may be robust for concurrently estimating BP and CIs.

Automatic Features Extraction Integrated With Exact Gaussian Process for Respiratory Rate and Uncertainty Estimations

Respiratory rate monitoring has become necessary for people with respiratory diseases, especially those living alone. Sudden changes in respiratory rate (RR) in these individuals may indicate a severe illness. Hence, estimating the RR is essential for cardiopulmonary health conditions. Capnography is a well-known standard technique for measuring RR. This study presents a novel methodology that uses an automatic feature extraction algorithm integrated with a robust feature selection technique based on exact Gaussian process regression (EGPR) for RR and uncertainty estimations. As EGPR is vital to noisy data and is naturally normalized, it can generate uncertainty estimates. Uncertainty estimation is essential for estimating physiological parameters. In the proposed methodology, data dimension is obtained using a power spectral density feature based on an autocorrelation function; subsequently, the long-resampled wave signal is split to increase the number of input data samples. Selecting an appropriate high-dimensional feature is necessary to predict a response effectively. Herein, we use a robust feature selection method based on neighbor component analysis. The proposed EGPR with automatic feature extraction is more accurate than conventional algorithms for RR and confidence intervals (CIs) estimations. The proposed methodology proves superior to conventional algorithms in terms of saving computer resources. We confirm that the proposed methodology, 2.085 breath per minute (bpm), and EGPR with robust feature selection, 2.049 bpm, show the lowest mean absolute error compared to the conventional algorithms in the Beth Israel Deaconess Medical Center data set.

Dual-Sensor Signals Based Exact Gaussian Process-Assisted Hybrid Feature Extraction and Weighted Feature Fusion for Respiratory Rate and Uncertainty Estimations.

Accurately estimating respiratory rate (RR) has become essential for patients and the elderly. Hence, we propose a novel method that uses exact Gaussian process regression (EGPR)-assisted hybrid feature extraction and feature fusion based on photoplethysmography and electrocardiogram signals to improve the reliability of accurate RR and uncertainty estimations. First, we obtain the power spectral features and use the multi-phase feature model to compensate for insufficient input data. Then, we combine four different feature sets and choose features with high weights using a robust neighbor component analysis. The proposed EGPR algorithm provides a confidence interval representing the uncertainty. Therefore, the proposed EGPR algorithm, including hybrid feature extraction and weighted feature fusion, is an excellent model with improved reliability for accurate RR estimation. Furthermore, the proposed EGPR methodology is likely the only one currently available that provides highly stable variation and confidence intervals. The proposed EGPR-MF, 0.993 breath per minute (bpm), and EGPR-feature fusion, 1.064 (bpm), show the lowest mean absolute error compared to the other models.

Designing Component Interfaces for the Circular Economy—A Case Study for Product-As-A-Service Business Models in the Automotive Industry

The resource-intensive automotive industry offers great potential to avoid waste through new circular business models. However, these new business models require technical innovations that enable the rapid dismantling of add-on parts. In this paper, we design new mechanical interfaces that enable fast and non-destructive dismantling while still fulfilling all technical requirements and develop a general model for the evaluation of disassembly capability. For this purpose, the current dismantling options of add-on parts are first examined and evaluated concerning defined KPIs using the example of the front bumper. Based on the analysis, the requirements as well as various solution principles for the new interface concept can be derived. The necessity of removing neighboring components is identified as the main challenge for rapid dismantling. Two different concepts for the interfaces were developed by inserting an intermediate level as a connecting part between the front bumper and the front module. We prove that by redesigning and reconstructing the interfaces the number of process steps required to remove the front bumper could be reduced by roughly 60% compared to current interface solutions. The developed methodology should be applied to other components of a vehicle to create a greater positive environmental, economic and societal impact.

A personalized DVH prediction model for HDR brachytherapy in cervical cancer treatment.

PurposeAlthough the knowledge-based dose-volume histogram (DVH) prediction has been largely researched and applied in External Beam Radiation Therapy, it is still less investigated in the domain of brachytherapy. The purpose of this study is to develop a reliable DVH prediction method for high-dose-rate brachytherapy plans.MethodA DVH prediction workflow combining kernel density estimation (KDE), k-nearest neighbor (kNN), and principal component analysis (PCA) was proposed. PCA and kNN were first employed together to select similar patients based on principal component directions. 79 cervical cancer patients with different applicators inserted was included in this study. The KDE model was built based on the relationship between distance-to-target (DTH) and the dose in selected cases, which can be subsequently used to estimate the dose probability distribution in the validation set. Model performance of bladder and rectum was quantified by |ΔD2cc|, |ΔD1cc|, |ΔD0.1cc|, |ΔDmax|, and |ΔDmean| in the form of mean and standard deviation. The model performance between KDE only and the combination of kNN, PCA, and KDE was compared.Result20, 30 patients were selected for rectum and bladder based on KNN and PCA, respectively. The absolute residual between the actual plans and the predicted plans were 0.38 ± 0.29, 0.4 ± 0.32, 0.43 ± 0.36, 0.97 ± 0.66, and 0.13 ± 0.99 for |ΔD2cc|, |ΔD1cc|, |ΔD0.1cc|, |ΔDmax|, and |ΔDmean| in the bladder, respectively. For rectum, the corresponding results were 0.34 ± 0.27, 0.38 ± 0.33, 0.63 ± 0.57, 1.41 ± 0.99 and 0.23 ± 0.17, respectively. The combination of kNN, PCA, and KDE showed a significantly better prediction performance than KDE only, with an improvement of 30.3% for the bladder and 33.3% for the rectum.ConclusionIn this study, a knowledge-based machine learning model was proposed and verified to accurately predict the DVH for new patients. This model is proved to be effective in our testing group in the workflow of HDR brachytherapy.