Search Results (3,154)

The real-time stress assessment of metal pressure components is one of the key factors in ensuring the safe operation of thermal power plants. To address the challenge of real-time prediction of stress in the key areas of complex special-shaped metal pressure-bearing components in a certain domestic 300 MW thermal power plant, three typical complex metal pressure-bearing components, the main steam pipe tee (MSPT), the steam drum downcomer joint (DDJ) and the header ligament (HL), were taken as research objects. The stress distribution of the three complex metal pressure-bearing components under different conditions was analyzed through the finite element method, and the stress results at the dangerous points were used as samples to establish training sample data. Subsequently, different machine learning methods were employed to train the sample data. The training results indicate that neural networks (NNs) and the Auto-Sklearn Regression (ASR) models can accurately predict the stress of the key parts of complex metal pressure-bearing components in real time. The ASR method demonstrates better performance in stress prediction of the main steam pipe tee, with a prediction accuracy of ≥96%. The NN model shows better prediction for the header ligament, with a prediction accuracy of ≥94%. These research findings provide effective support for the high-temperature lifespan assessment and safe operation of thermal power plants. Full article

The rehabilitation exoskeleton represents a typical human–robot system featuring complex nonlinear dynamics. This paper is devoted to proposing an adaptive impedance control strategy for a rehabilitation exoskelton. The patient’s motion intention is estimated online by the neural network (NN) to cope with the intervention of the patient’s subjective motor awareness in the late stage of rehabilitation training. Due to the differences in impedance parameters for training tasks in individual patients and periods, the least square method was used to learn the impedance parameters of the patient. Considering the uncertainties of the exoskeleton and the safety of rehabilitation training, an adaptive neural network impedance controller with output constraints was designed. The NN was applied to approximate the unknown dynamics and the barrier Lyapunov function was applied to prevent the system from violating the output rules. The feasibility and effectiveness of the proposed strategy were verified by simulation. Full article

There is a growing need to establish a breed reassessment system responding to tomato spotted wilt virus (TSWV) mutations. Conventional visual survey methods allow for assessing TSWV severity and disease incidence, while enzyme-linked Immunosorbent Assay (ELISA) data analysis can replace and validate visual surveys. This study proposes a non-destructive evaluation technique for TSWV using an open software platform based on image processing and machine learning. Many studies have evaluated resistance to the TSWV. However, as strains that destroy TSWV resistance emerge, an evaluation technique that can identify new genetic resources with resistance to the variants is needed. Evaluation techniques based on images and machine learning have the strength to respond quickly and accurately to the emergence of new variants. However, studies on resistance to viruses rely on empirical judgment based on visual surveys. The accuracy of the training model using Support Vector Machine (SVM), Logistic Regression (LR), and neural networks (NNs) was excellent, in the following order: NNs (0.86), LR (0.81), SVM (0.65). Meanwhile, the accuracy of the validation model was good, in the following order NN (0.84), LR (0.79), SVM (0.71). NNs’ prediction performance was verified through ELISA data analysis, showing a causal relationship between the two data sets with an R² of 0.86 with statistical significance. Imaging and NN-based TSWV resistance assessment technologies show significant potential as key tools in genetic resource reassessment systems that ensure a rapid and accurate response to the emergence of new TSWV strains. Full article

This paper overviews gas phase experiments with respect to one fundamental part of nuclear magnetic resonance (NMR) spectra. Indirect spin-spin coupling is an important parameter of NMR spectra and is observed as the splitting of spectral signals. A molecule containing two different magnetic nuclei (e.g., hydrogen HD, HT, or DT) exhibits this interaction in an external magnetic field measured as the spin-spin coupling parameter, ⁿJ(NN′). Modern quantum chemical methods allow the precise calculation of spin-spin coupling, but it is never easy because ⁿJ(NN′) is modified by temperature and intermolecular interactions. Accurate calculations can be performed only for small isolated molecules. NMR spectroscopy can deliver measurements of spin-spin couplings for isolated molecules if ⁿJ(NN′) parameters are observed in the gas phase as a function of density. The extrapolation of such measurements to the zero-density limit permits ⁿJ₀(NN′) determination free from intermolecular interactions. The latter technique can also be applied to liquid vapors in molecules like acetonitrile or water. Spin-spin couplings across one chemical bond (¹J₀(NN′)) are the largest and most important for theoretical modeling. The present review reports numerous ¹J₀(NN′) parameters recently measured by multinuclear NMR spectra of gaseous samples. Full article

The structural aspects of homo-chromophores in Cu(I)(XXX) complexes, where X₃ = N₃, C₃, Cl₃, S₃, P₃, Br₃, or I₃, are analyzed in this study. These copper(I) derivatives crystallize in five distinct crystal systems as follows: rhombohedral (1 example), trigonal (1 example), orthorhombic (4 examples), triclinic (5 examples), and monoclinic (15 examples). The angular distortion from regular trigonal geometry increases in the following order: Cu(ClClCl) < Cu(NNN) < Cu(PPP) < Cu(BrBrBr) < Cu(III) < Cu(CCC) < Cu(SSS). For Cu(I)(XX) complexes, the deviation from linear geometry increases in the order: Cu(SeSe) < Cu(SS) < Cu(OO) < Cu(ClCl) < Cu(NN) < Cu(CC) < Cu(PP) < Cu(BrBr). The structural parameters of Cu(I)(XXX) are examined, discussed, and compared with those of homonuclear Cu(I)(XX) complexes. Full article

Background/Objectives: There are various challenges in the segmentation of anatomical structures with artificial intelligence due to the different structural features of the relevant region/tissue. The aim of this study was to detect the nasolacrimal canal (NLC) using the nnU-Net v2 convolutional neural network (CNN) model in cone beam-computed tomography (CBCT) images and to evaluate the successful performance of the model in automatic segmentation. Methods: CBCT images of 100 patients were randomly selected from the data archive. The raw data were transferred to the 3D Slicer imaging software in DICOM format (Version 4.10.2; MIT, Massachusetts, USA). NLC was labeled using the polygonal type of manual method. The dataset was split into training, validation and test sets in a ratio of 8:1:1. nnU-Net v2 architecture was applied to the training and test datasets to predict and generate appropriate algorithm weight factors. The confusion matrix was used to check the accuracy and performance of the model. As a result of the test, the Dice Coefficient (DC), Intersection over Union (IoU), F1-Score and 95% Hausdorff distance (95% HD) metrics were calculated. Results: By testing the model, DC, IoU, F1-Scores and 95% HD metric values were found to be 0.8465, 0.7341, 0.8480 and 0.9460, respectively. According to the data obtained, the receiver-operating characteristic (ROC) curve was drawn and the AUC value under the curve was determined to be 0.96. Conclusions: These results showed that the proposed nnU-Net v2 model achieves NLC segmentation on CBCT images with high precision and accuracy. The automated segmentation of NLC may assist clinicians in determining the surgical technique to be used to remove lesions, especially those affecting the anterior wall of the maxillary sinus. Full article

Quantification of pain or discomfort induced by pressure is essential for understanding human responses to physical stimuli and improving user interfaces. Pain research has been conducted to investigate physiological signals associated with discomfort and pain perception. This study analyzed changes in electrodermal activity (EDA), tissue oxygen saturation (StO₂), heart rate variability (HRV), and Visual Analog Scale (VAS) under pressures of 10, 20, and 30 kPa applied for 3 min to the thigh, knee, and calf in a seated position. Twenty participants were tested, and relationships between biosignals, pressure intensity, and pain levels were evaluated using Friedman tests and post-hoc analyses. Multiple linear regression models were used to predict VAS and pressure, and five machine learning models (SVM, Logistic Regression, Random Forest, MLP, KNN) were applied to classify pain levels (no pain: VAS 0, low: VAS 1–3, moderate: VAS 4–6, high: VAS 7–10) and pressure intensity. The results showed that higher pressure intensity and pain levels affected sympathetic nervous system responses and tissue oxygen saturation. Most EDA features and StO₂ significantly changed according to pressure intensity and pain levels, while NN interval and HF among HRV features showed significant differences based on pressure intensity or pain level. Regression analysis combining biosignal features achieved a maximum R² of 0.668 in predicting VAS and pressure intensity. The four-level classification model reached an accuracy of 88.2% for pain levels and 81.3% for pressure intensity. These results demonstrated the potential of EDA, StO₂, HRV signals, and combinations of biosignal features for pain quantification and prediction. Full article

Currently, iron-catalyzed low-valent sulfur species processes are regarded as potentially valuable advanced oxidation processes (AOPs) in wastewater treatment. As a commonly used low-valent sulfur species in the food industry, metabisulfite (MBS) can undergo decomposition to bisulfite when dissolved in water. Therefore, the combination of MBS with dissolved Fe(III) at environmentally relevant concentrations is proposed in this study to accelerate organic contaminants degradation while simultaneously minimizing the production of iron sludge. The results show that the Fe(III)/MBS process could degrade various organic contaminants, including acid orange 7 (AO7), and the removal efficiency of AO7 obeyed the pseudo-first-order kinetic. The rate constant values exhibited variations depending on the initial concentrations of Fe(III) and MBS, pH values, as well as the reaction temperature. Moreover, the contribution of HO^• and SO₄^•− to AO7 degradation was estimated as 51.59% and 46.45%, respectively. Furthermore, Cl⁻ showed a minimal effect while HCO₃⁻ and humic acid resulted in a significant inhibitory effect on AO7 degradation. The satisfactory degradation of AO7 was achieved in three real water bodies. Ultimately, the results of gas chromatography–mass spectrometry and the theoretical calculations greatly facilitate the proposal of AO7 degradation pathways, including N=N cleavage, hydroxylation, and hydrogen abstraction. The findings of this study indicate that the Fe(III)/MBS process may be a promising AOP for further application in organic contaminants degradation during wastewater treatment. Full article

The global adoption of hybrid renewable energy systems (HRESs) is accelerating as a strategic response to escalating energy demands and the imperative to mitigate greenhouse gas emissions. Despite the development of various technological tools, such as pre-feasibility analysis, sizing, and simulation tools, challenges persist due to their limited flexibility in modifying system architectures and their typically long computation times, which hinder their practical efficiency. This study introduces a novel hybrid method that integrates the Branch and Bound (BB) heuristic search algorithm with the k-Nearest Neighbors (kNN) algorithm to drastically reduce the simulation time of microgrid models in Simulink. Validation considering four distinct case studies reveals that our method can decrease the simulation time by up to 94.68% while maintaining an acceptable accuracy. Specifically, simulation times in certain cases were reduced from approximately 21,780 and 118,580 s to 1442.7969 and 6306.0625 s, respectively. This significant reduction facilitates the rapid evaluation and selection of optimal HRES configurations, enhancing the efficiency of both editable and non-editable systems. Through streamlining the simulation process, this approach not only accelerates the design and analysis phases but also supports the broader adoption and deployment of HRESs, which is critical for achieving a sustainable future. This advancement offers a robust and efficient methodology for optimizing simulation times, thereby addressing a key bottleneck in the development and implementation of hybrid renewable energy solutions. Full article

Objectives: To quantify the clinical value of integrating a commercially available artificial intelligence (AI) algorithm for intracranial aneurysm detection in a screening setting that utilizes cranial magnetic resonance imaging (cMRI) scans acquired primarily for other clinical purposes. Methods: A total of 907 consecutive cMRI datasets, including time-of-flight-angiography (TOF-MRA), were retrospectively identified from patients unaware of intracranial aneurysms. cMRIs were analyzed by a commercial AI algorithm and reassessed by consultant-level neuroradiologists, who provided confidence scores and workup recommendations for suspicious findings. Patients with newly identified findings (relative to initial cMRI reports) were contacted for on-site consultations, including cMRI follow-up or catheter angiography. The number needed to screen (NNS) was defined as the cMRI quantity that must undergo AI screening to achieve various clinical endpoints. Results: The algorithm demonstrates high sensitivities (100% for findings >4 mm in diameter), a 17.8% MRA alert rate and positive predictive values of 11.5–43.8% (depending on whether inconclusive findings are considered or not). Initial cMRI reports missed 50 out of 59 suspicious findings, including 13 certain intradural aneurysms. The NNS for additionally identifying highly suspicious and therapeutically relevant (unruptured intracranial aneurysm treatment scores balanced or in favor of treatment) findings was 152. The NNS for recommending additional follow-/workup imaging (cMRI or catheter angiography) was 26, suggesting an additional up to 4% increase in imaging procedures resulting from a preceding AI screening. Conclusions: AI-powered routine screening of cMRIs clearly lowers the high risk of incidental aneurysm non-reporting but results in a substantial burden of additional imaging follow-up for minor or inconclusive findings. Full article

The advent of two-dimensional (2D) ferroelectrics offers a new paradigm for device miniaturization and multifunctionality. Recently, 2D α-In₂Se₃ and related III–VI compound ferroelectrics manifest room-temperature ferroelectricity and exhibit reversible spontaneous polarization even at the monolayer limit. Here, we employ first-principles calculations to investigate group-III selenide van der Waals (vdW) heterojunctions built up by 2D α-In₂Se₃ and α-Ga₂Se₃ ferroelectric (FE) semiconductors, including structural stability, electrostatic potential, interfacial charge transfer, and electronic band structures. When the FE polarization directions of α-In₂Se₃ and α-Ga₂Se₃ are parallel, both the α-In₂Se₃/α-Ga₂Se₃ P↑↑ (UU) and α-In₂Se₃/α-Ga₂Se₃ P↓↓ (NN) configurations possess strong built-in electric fields and hence induce electron–hole separation, resulting in carrier depletion at the α-In₂Se₃/α-Ga₂Se₃ heterointerfaces. Conversely, when they are antiparallel, the α-In₂Se₃/α-Ga₂Se₃ P↓↑ (NU) and α-In₂Se₃/α-Ga₂Se₃ P↑↓ (UN) configurations demonstrate the switchable electron and hole accumulation at the 2D ferroelectric interfaces, respectively. The nonvolatile characteristic of ferroelectric polarization presents an innovative approach to achieving tunable n-type and p-type conductive channels for ferroelectric field-effect transistors (FeFETs). In addition, in-plane biaxial strain modulation has successfully modulated the band alignments of the α-In₂Se₃/α-Ga₂Se₃ ferroelectric heterostructures, inducing a type III–II–III transition in UU and NN, and a type I–II–I transition in UN and NU, respectively. Our findings highlight the great potential of 2D group-III selenides and ferroelectric vdW heterostructures to harness nonvolatile spontaneous polarization for next-generation electronics, nonvolatile optoelectronic memories, sensors, and neuromorphic computing. Full article

Monitoring the condition of rolling bearings is a crucial task in many industries. An efficient tool for diagnosing bearing defects is necessary since they can lead to complete machine failure and significant economic losses. Traditional diagnosis solutions often rely on a complex artificial feature extraction process that is time-consuming, computationally expensive, and too complex to deploy in practice. In actual working conditions, however, the amount of labeled fault data available is relatively small, so a deep learning model with good generalization and high accuracy is difficult to train. This paper proposes a solution that uses a simple feedforward artificial neural network (NN) for classification and adopts the linear predictive coding (LPC) algorithm for feature extraction. The LPC algorithm finds several coefficients for a given signal segment containing information about the signal spectrum, which is sufficient for further classification. The LPC-NN solution was tested on the Case Western Reserve University (CWRU) and South Ural State University (SUSU) datasets. The results demonstrated that, in most cases, LPC-NN yielded an accuracy of 100%. The proposed method achieves higher diagnostic accuracy and stability to load changes than other advanced techniques, has a significantly improved time performance, and is conducive to real-time industrial fault diagnosis. Full article

In contemporary business environments, manufacturing companies must continuously enhance their performance to ensure competitiveness. Material feeding systems are of pivotal importance in the optimization of productivity, with attendant improvements in quality, reduction of costs, and minimization of delivery times. This study investigates the selection of material feeding methods, including Kanban, line-storage, call-out, and kitting systems, within a manufacturing company. The research employs six machine learning (ML) algorithms—logistic regression (LR), decision trees (DT), random forest (RF), support vector machines (SVM), K-nearest neighbors (K-NN), and artificial neural networks (ANN)—to develop a multi-class classification model for material feeding system selection. Utilizing a dataset comprising 2221 materials and an 8-fold cross-validation technique, the ANN model exhibits superior performance across all evaluation metrics. Shapley values analysis is employed to elucidate the influence of pivotal input parameters within the selection process for material feeding systems. This research provides a comprehensive framework for material feeding system selection, integrating advanced ML models with practical manufacturing insights. This study makes a significant contribution to the field by enhancing decision-making processes, optimizing resource utilization, and establishing the foundation for future studies on adaptive and scalable material feeding strategies in dynamic industrial environments. Full article

Background: Intraoperative ultrasound (ioUS) provides real-time imaging during neurosurgical procedures, with advantages such as portability and cost-effectiveness. Accurate tumor segmentation has the potential to substantially enhance the interpretability of ioUS images; however, its implementation is limited by persistent challenges, including noise, artifacts, and anatomical variability. This study aims to develop a convolutional neural network (CNN) model for glioma segmentation in ioUS images via a multicenter dataset. Methods: We retrospectively collected data from the BraTioUS and ReMIND datasets, including histologically confirmed gliomas with high-quality B-mode images. For each patient, the tumor was manually segmented on the 2D slice with its largest diameter. A CNN was trained using the nnU-Net framework. The dataset was stratified by center and divided into training (70%) and testing (30%) subsets, with external validation performed on two independent cohorts: the RESECT-SEG database and the Imperial College NHS Trust London cohort. Performance was evaluated using metrics such as the Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and 95th percentile Hausdorff distance (HD95). Results: The training cohort consisted of 197 subjects, 56 of whom were in the hold-out testing set and 53 in the external validation cohort. In the hold-out testing set, the model achieved a median DSC of 0.90, ASSD of 8.51, and HD95 of 29.08. On external validation, the model achieved a DSC of 0.65, ASSD of 14.14, and HD95 of 44.02 on the RESECT-SEG database and a DSC of 0.93, ASSD of 8.58, and HD95 of 28.81 on the Imperial-NHS cohort. Conclusions: This study supports the feasibility of CNN-based glioma segmentation in ioUS across multiple centers. Future work should enhance segmentation detail and explore real-time clinical implementation, potentially expanding ioUS’s role in neurosurgical resection. Full article

Leveraging satellite monitoring and machine learning (ML) techniques for water clarity assessment addresses the critical need for sustainable water management. This study aims to assess water clarity by predicting the Secchi disk depth (SDD) using satellite images and ML techniques. The primary methods involve data preparation and SSD inference. During data preparation, AquaSat samples, originally from the L1TP collection, were updated with the Landsat 8 satellite’s latest postprocessing, L2SP, which includes atmospheric corrections, resulting in 33,261 multispectral observations and corresponding SSD measurements. For inferring the SSD, regressors such as SVR, NN, and XGB, along with an ensemble of them, were trained. The ensemble demonstrated performance with an average determination coefficient of $R^2$ of around 0.76 and a standard deviation of around 0.03. Field data validation achieved an $R^2$ of 0.80. Furthermore, we show that the regressors trained with L1TP imagery for predicting SSD result in a favorable performance with respect to their counterparts trained on the L2SP collection. This document contributes to the transition from semi-analytical to data-driven methods in water clarity research, using an ML ensemble to assess the clarity of water bodies through satellite imagery. Full article