Identification Of States Research Articles

Manifold regularized deep canonical variate analysis with interpretable attribute guidance for three-phase flow process monitoring

Oil–gas–water three-phase flow has multiple flow states, exhibiting dynamic, nonlinear, and instantaneous behaviors. Monitoring and analysis of flow state are crucial for ensuring safe operation of industrial processes. However, the absence of a universal definition for three-phase flow states, owing to the complex flow characteristics and structures, leads to that labeling three-phase flow states by categories is impractical. For comprehensive monitoring and analysis, the flow states should be described from both global (e.g., the dominant phase) and local (e.g., the interphase structure) aspects. Given the aforementioned challenges, the work aims to address the accurate identification of typical three-phase flow states and to meticulously monitor and analyze the continuous evolutionary process of three-phase flow. To achieve it, the manifold regularized deep canonical variate analysis with attribute guidance (Ag-MRDCVA) is proposed, which not only introduces an innovative monitoring paradigm that designs state attributes for process description but also facilitates knowledge transfer from typical flow states to transition states. For enhancing the model’s feature embedding capabilities, an attribute guidance module is introduced to increase supervision information at both class and attribute levels. Then, convolutional neural network (CNN) backbones are developed with a dual objective of global serial correlation and local manifold regularization, which capture spatiotemporal information at both global and local scales, facilitating effective attribute encoding and characterization of flow states. Finally, the attribute evolution heat map and a monitoring metric (MM) collectively offer a compelling and comprehensive analysis of the three-phase flow process. Extensive experiments demonstrate that Ag-MRDCVA surpasses existing methods, showcasing its ability to minutely monitor the process while providing reasonable explanations.

A novel bilateral stream neural network for melt pool monitoring during laser direct energy deposition

Detection and classification methods for the melt pool state in laser direct energy deposition (L-DED) can significantly help predict defects and mechanical properties of L-DED metal parts. Although traditional machine learning algorithms based on physical modeling methods and convolutional neural networks have recently been introduced into melt pool state identification, these methods rely on complex artificially designed features or cannot simultaneously detect defects in multiple dimensions. In this paper, a novel bilateral stream neural network was designed for melt pool identification, which performs defect identification in two label dimensions simultaneously. Two sets of single-channel experiments were designed to collect the dataset captured by a high-speed camera. By cutting the metal parts and marking them with professional equipment operated by professionals, the dataset was labeled according to the bonding condition and dilution rate criteria. Without an additive model structure, the model achieved 95.2% accuracy in identifying defects in the bonding condition and 92.8% in determining deficiencies in the dilution rate. In order to explain the identification mechanism of the model, the CAM method was utilized for the visual display of the model recognition process, which provides a potential application solution for the online monitoring method of the L-DED.

Epithelial cell states associated with kidney and allograft injury.

The kidney epithelium, with its intricate arrangement of highly specialized cell types, constitutes the functional core of the organ. Loss of kidney epithelium is linked to the loss of functional nephrons and a subsequent decline in kidney function. In kidney transplantation, epithelial injury signatures observed during post-transplantation surveillance are strong predictors of adverse kidney allograft outcomes. However, epithelial injury is currently neither monitored clinically nor addressed therapeutically after kidney transplantation. Several factors can contribute to allograft epithelial injury, including allograft rejection, drug toxicity, recurrent infections and postrenal obstruction. The injury mechanisms that underlie allograft injury overlap partially with those associated with acute kidney injury (AKI) and chronic kidney disease (CKD) in the native kidney. Studies using advanced transcriptomic analyses of single cells from kidney or urine have identified a role for kidney injury-induced epithelial cell states in exacerbating and sustaining damage in AKI and CKD. These epithelial cell states and their associated expression signatures are also observed in transplanted kidney allografts, suggesting that the identification and characterization of transcriptomic epithelial cell states in kidney allografts may have potential clinical implications for diagnosis and therapy.

MiRNAs from the Dlk1-Dio3 locus and miR-224/452 cluster contribute to glioblastoma tumor heterogeneity

Glioblastoma is one of the most common and aggressive brain tumors and has seen few improvements in patient outcomes. Inter-tumor heterogeneity between tumors of different patients as well as intra-tumor heterogeneity of cells within the same tumor challenge the development of effective drugs. MiRNAs play an essential role throughout the developing brain and regulate many key genes involved in oncogenesis, yet their role in driving many of the processes underlying tumor heterogeneity remains unclear. In this study, we highlight miRNAs from the Dlk1-Dio3 and miR-224/452 clusters which may be expressed cell autonomously and have expression that is associated with cell state genes in glioblastoma, most prominently in neural progenitor-like and mesenchymal-like states respectively. These findings implicate these miRNA clusters as potential regulators of glioblastoma intra-tumoral heterogeneity and may serve as valuable biomarkers for cell state identification.

An improved YOLOv7 for the state identification of sliding chairs in railway turnout

The sliding chairs are important components that support the switch rail conversion in the railway turnout. Due to the harsh environmental erosion and the attack from the wheel vibration, the failure rate of the sliding chairs accounts for up to 10% of the total failure number in turnout. However, there is little research carried out in the existing literature to diagnose the deterioration states of the sliding chairs. To fill out this gap, by utilizing the images containing the sliding chairs, we propose an improved You Only Look Once version 7 (YOLOv7) to identify the state of the sliding chairs. Specifically, to meet the challenge brought by the small inter-class differences among the sliding chair states, we first integrate the Convolutional Block Attention Module (CBAM) into the YOLOv7 backbone to screen the information conducive to state identification. Then, an extra detector for a small object is customized into the YOLOv7 network in order to detect the small-scale sliding chairs in images. Meanwhile, we revise the localization loss in the objective function as the Efficient Intersection over Union (EIoU) to optimize the design of the aspect ratio, which helps the localization of the sliding chairs. Next, to address the issue caused by the varying scales of the sliding chairs, we employ K-means++ to optimize the priori selection of the initial anchor boxes. Finally, based on the images collected from real-world turnouts, the proposed method is verified and the results show that our method outperforms the basic YOLOv7 in the state identification of the sliding chairs with 4% improvements in terms of both mean Average Precision@0.5 (mAP@0.5) and F1-score.

Neural correlates of recalled sadness, joy, and fear states: a source reconstruction EEG study.

The capacity to understand the others' emotional states, particularly if negative (e.g. sadness or fear), underpins the empathic and social brain. Patients who cannot express their emotional states experience social isolation and loneliness, exacerbating distress. We investigated the feasibility of detecting non-invasive scalp-recorded electrophysiological signals that correspond to recalled emotional states of sadness, fear, and joy for potential classification. The neural activation patterns of 20 healthy and right-handed participants were studied using an electrophysiological technique. Analyses were focused on the N400 component of Event-related potentials (ERPs) recorded during silent recall of subjective emotional states; Standardized weighted Low-resolution Electro-magnetic Tomography (swLORETA) was employed for source reconstruction. The study classified individual patterns of brain activation linked to the recollection of three distinct emotional states into seven regions of interest (ROIs). Statistical analysis (ANOVA) of the individual magnitude values revealed the existence of a common emotional circuit, as well as distinct brain areas that were specifically active during recalled sad, happy and fearful states. In particular, the right temporal and left superior frontal areas were more active for sadness, the left limbic region for fear, and the right orbitofrontal cortex for happy affective states. In conclusion, this study successfully demonstrated the feasibility of detecting scalp-recorded electrophysiological signals corresponding to internal and subjective affective states. These findings contribute to our understanding of the emotional brain, and have potential applications for future BCI classification and identification of emotional states in LIS patients who may be unable to express their emotions, thus helping to alleviate social isolation and sense of loneliness.

Input/output corrective control of switched asynchronous sequential machines under arbitrary switching

This article presents a novel modeling and corrective control of switched asynchronous sequential machines (ASMs) with input/output submachines. In particular, the considered switched ASM is harnessed by the external switching signal that provokes arbitrary change of the mode or submachine. The existence of the external switching signal imposes unpredictable state drift opposite to model matching as well as ambiguity in determining the active submachine. We first present a state observer for state observation and identification of the active submachine. Based on the information delivered by the observer, we address the existence condition and design procedure for a corrective controller that matches the stable-state behavior of the closed-loop system to that of a reference model for every possible switching sequence. To demonstrate the validity and applicability of the proposed control scheme, we conduct hardware experiments on field-programmable gate array (FPGA) circuits for a space-borne digital system and provide convincing experimental results.

Optimal Parameter Estimation Techniques for Complex Nonlinear Systems

AbstractAccurate parameter estimation and state identification within nonlinear systems are fundamental challenges addressed by optimization techniques. This paper fills a critical gap in previous research by investigating tailored optimization methods for parameter estimation in nonlinear system modeling, with a particular emphasis on chaotic dynamical systems. We introduce and compare three optimization methods: a gradient-based iterative algorithm, the Levenberg-Marquardt algorithm, and the Nelder-Mead simplex method. These methods are strategically employed to simplify complex nonlinear optimization problems, rendering them more manageable. Through a comprehensive exploration of the performance of these methods in determining parameters across diverse systems, including the van der Pol oscillator, the Rössler system, and pharmacokinetic modeling, our study revealed that the accuracy and reliability of the Nelder-Mead simplex method were consistent. The Nelder-Mead simplex algorithm emerged as a powerful tool, that consistently outperforms alternative methods in terms of root mean squared error (RMSE) and convergence reliability. Visualizations of trajectory comparisons and parameter convergence under various noise levels further emphasize the algorithm’s robustness. These studies suggest that the Nelder-Mead simplex method has potential as a valuable tool for parameter estimation in chaotic dynamical systems. Our study’s implications extend beyond theoretical considerations, offering promising insights for parameter estimation techniques in diverse scientific fields reliant on nonlinear system modeling.

Automatic sleep-wake classification and Parkinson's disease recognition using multifeature fusion with support vector machine.

Sleep disturbance is a prevalent nonmotor symptom of Parkinson's disease (PD), however, assessing sleep conditions is always time-consuming and labor-intensive. In this study, we performed an automatic sleep-wake state classification and early diagnosis of PD by analyzing the electrocorticography (ECoG) and electromyogram (EMG) signals of both normal and PD rats. The study utilized ECoG power, EMG amplitude, and corticomuscular coherence values extracted from normal and PD rats to construct sleep-wake scoring models based on the support vector machine algorithm. Subsequently, we incorporated feature values that could act as diagnostic markers for PD and then retrained the models, which could encompass the identification of vigilance states and the diagnosis of PD. Features extracted from occipital ECoG signals were more suitable for constructing sleep-wake scoring models than those from frontal ECoG (average Cohen's kappa: 0.73 vs. 0.71). Additionally, after retraining, the new models demonstrated increased sensitivity to PD and accurately determined the sleep-wake states of rats (average Cohen's kappa: 0.79). This study accomplished the precise detection of substantia nigra lesions and the monitoring of sleep-wake states. The integration of circadian rhythm monitoring and disease state assessment has the potential to improve the efficacy of therapeutic strategies considerably.

Complex Flexible Analytic Wavelet Transform for UAV State Identification Using RF Signal

Complex Flexible Analytic Wavelet Transform for UAV State Identification Using RF Signal

Emotion detection from handwriting and drawing samples using an attention-based transformer model.

Emotion detection (ED) involves the identification and understanding of an individual's emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person's emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data obtained provides a set of points that correspond to the handwriting or drawing strokes. Each stroke point is subsequently delivered to the attention-based transformer model, which embeds it into a high-dimensional vector space. The model builds a prediction about the emotional state of the person who generated the sample by integrating the most important components and patterns in the input sequence using self-attentional processes. The proposed approach possesses a distinct advantage in its enhanced capacity to capture long-range correlations compared to conventional recurrent neural networks (RNN). This characteristic makes it particularly well-suited for the precise identification of emotions from samples of handwriting and drawings, signifying a notable advancement in the field of emotion detection. The proposed method produced cutting-edge outcomes of 92.64% on the benchmark dataset known as EMOTHAW (Emotion Recognition via Handwriting and Drawing).

Abstract 5496: Multi-omic spatial analysis of the tumor microenvironment in gliomas

Abstract Introduction: The diverse tumor environment of high-grade glioma, which remains refractory to treatment, demands new innovative, multi-omic approaches to characterize tumor heterogeneity and expression profiles associated with progression and therapeutic response. Approach: We have applied an integrated, multi-omic approach using paired spatial in situ sequencing (Xenium) and protein profiling (PhenoCycler® Fusion) of glioma tissue to identify distinct glioma cellular phenotypes, activation states and metabolic pathways at true single cell resolution. Our multi-omic approach used hundreds of phenotypic RNA target probes and >50 antibodies in an unbiased single-cell analysis of primary, recurrent, and IDH1 mutant/wild type tumors with a range of heterogeneity. Summary: We were able to distinguish differentially regulated genes and pathways between aggregated primary and recurrent GBM, including cell cycle pathways being upregulated in primary vs recurrent GBM glial cell clusters and a down regulation of ERBB4 signaling in vascular cell clusters in primary GBMs. Our data also suggests that SOX11, known to be involved in tumorigenesis, is >2-fold upregulated in annotated tumor cells in primary compared to recurrent tumors. We were also able to quantitatively localize the expression of Epidermal Growth Factor Receptor, variant III (EGFRvIII) to specific tumor-cell sub-types, which is important given its association with therapeutic resistance. Further, we have been able to establish the cellular neighborhoods and cell-cell and receptor-ligand relationships within the tumors. Our spatial protein analysis identified distinct tumor and immune phenotypes with varying abundance of myeloid and lymphoid populations in IDH1 wt and mt tumors. Moreover, spatial proximity analyses and cellular neighborhood analyses revealed differences in higher order organizational landscapes that may contribute to the differential outcomes across wt and mt tumor subtypes. Conclusion: Multiomic spatial analysis enables deeper characterization of the glioma cellular and functional landscape to broaden our understanding of the key TME features that contribute to disease pathogenesis and prognoses. Our study provides an analytical framework to combine RNA and protein-based spatial data for a holistic investigation into a variety of glioma subtypes and aid in the identification of novel biomarkers, spatial neighborhoods, and functional states that drive glioma progression. Citation Format: Dmytro Klymyshyn, Vaibhav Jain, Lauren Whaley, Emily Hocke, Bassem Ben Cheikh, Alan Smith, Karen Abramson, Nadine Nelson, Diane Satterfield, Elizabeth Thomas, Giselle Lopez, Seetha Hariharan, Michael Brown, Niyati Jhaveri, Roger McLendon, David Ashley, Matthew Waitkus, Simon Gregory. Multi-omic spatial analysis of the tumor microenvironment in gliomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5496.

Estimating the economic effects of US state and local fiscal policy: A synthetic control method matching‐regression approach

AbstractIn this paper, we advance the empirical literature on US state and local fiscal policymaking by using the synthetic control method (SCM) to create pairwise matches for states in subsequent regression analysis of the relationships between state and local fiscal policies and several state economic outcomes. Additional contributions include the use of principal component analysis to construct broader narratives of state economic performance and to reduce the dimensionality of the characteristics used in SCM matching, while the regressions also include variables to control for post‐matching economic shocks. Compared to conventional regression analysis, the SCM matching‐regression approach better addresses potential endogeneity, reduces interpolation bias, and creates fiscal policy measures that better reflect policy differences. The SCM‐matched regressions produce more statistically significant relationships between state and local fiscal variables and economic outcomes than do the conventional unmatched regressions, suggesting improved identification of state and local fiscal policy effects on economic outcomes. Robust relationships found include negative economic effects of the own‐source revenue burden and property taxes. Consistent with the existing literature, the estimated fiscal policy effects are quantitatively small and unlikely to drive differences in state economic performance.

Exploring the Feasibility of Applying Deep Learning for the Early Prediction of Arthritis

Introduction: Arthritis is one of the most common chronic diseases. Early detection of arthritis and its progression can facilitate early intervention measures, lowering disease severity in patients. As electronic health records (EHR) become more accessible, this study assesses whether general health information and arthritis-related questionnaires can be used in arthritis diagnosis, without the involvement of costly imaging methods. Therefore, we created deep learning (DL) and machine learning (ML) models to explore the feasibility of combining EHR and modern computational tools to diagnose arthritis. Methods: A total of 782 arthritis patients and 4014 control patients were identified from the Osteoarthritis Initiative (OAI) – a ten-year-long observational study that included patient EHR in five time points. Six hundred variables were filtered by random forest classifier followed by manual filtering. Data were split properly to training, testing and validation set, and the training set was balanced. Sequential, nonsequential DL models, and five independent DL models for each time points were used. The accuracy, positive prevalence value (PPV), negative prevalence value (NPV), and area under curve (AUC), were assessed and compared with four classical ML models. SHAP (SHapley Additive exPlanations) summary analysis was also conducted. Results: Sequential and non-sequential deep learning models showed accuracies of ~ 0.97, and the four classical machine learning approaches showed accuracies of above 0.9. High positive and negative predicted values (> 0.90) for all of the models suggested the potential clinical applicability of the model, while the SHAP analysis demonstrated its interpretability. Discussion: We tested various models and showed the ability to use machine learning methods for early diagnosis of arthritis with EHR. The models can be used as a screening tool to select susceptible patients for confirmatory tests such as X-ray and MRI. Identification of early disease states could facilitate protective measures that slow disease progression.

Identification of the allelic state of the Rca2 gene for resistance to anthracnose and determination of genetic relatedness in garden strawberry genotypes

The studies were conducted between 2022-2023. The purpose of the work was to analyze the relationship of hybrids and varieties of strawberries resistant and susceptible to anthracnose and to determine the putative allelic status of the anthracnose resistance gene Rca2. Selection of ISSR primers for identifying polymorphism and constructing dendrites of genetic similarity of 19 garden strawberry genotypes from the collection of the Federal State Budgetary Scientific Institution Federal Scientifi c Center for Horticulture. Th e eff ectiveness of ISSR primers was assessed by the ISSR-PCR method, followed by gel electrophoresis of ISSR-PCR products in an agarose gel. To assess the allelic state of the Rca2 gene, the SCAR marker STS-Rca2_240 was used. The genetic distance of the analyzed strawberry samples was determined using the Jaccard coeffi cient. Cluster analysis was carried out using the STATISTICA program (StatSoft ). The putative allelic status of the anthracnose resistance gene Rca2 in the studied varieties and hybrids was established. Diagnostic fragment of the STS-Rca2_240 marker, 240 bp in size. was found in the Alpha variety. Based on an array of genetic similarity coefficients, dendrograms were constructed, during the analysis of which the relationship of the studied variety samples was established. Promising forms were identifi ed that had a high degree of genetic similarity to varieties resistant to anthracnose, and forms that were closely related to susceptible varieties.

Editorial: Recent advances in the identification of stress-strain states of materials

Editorial: Recent advances in the identification of stress-strain states of materials

Tool Wear State Identification Based on the IWOA-VMD Feature Selection Method

Complex, thin-walled components are the most important load-bearing structures in aircraft equipment. Monitoring the wear status of milling cutters is critical for enhancing the precision and efficiency of thin-walled item machining. The cutting force signals of milling cutters are non-stationary and non-linear, making it difficult to detect wear stages. In response to this issue, a system for monitoring milling cutter wear has been presented, which is based on parameterized Variational Mode Decomposition (VMD) Multiscale Permutation Entropy. Initially, an updated whale optimization technique is used, with the joint correlation coefficient serving as the fitness value for determining the VMD parameters. The improved VMD technique is then used to break down the original signal into a series of intrinsic mode functions, and the Multiscale Permutation Entropy of each effective mode is determined to generate a feature vector. Finally, a 1D Convolutional Neural Network (1D CNN) is employed as the input model for state monitoring using the feature vector. The experimental findings show that the suggested technique can efficiently extract characteristics indicating the wear condition of milling cutters, allowing for the precise monitoring of milling cutter wear states. The recognition rate is as high as 98.4375%, which is superior to those of comparable approaches.

A method for real-time identification of muck flow state inside EPB shield chambers in fine-grained soils

This study aims to evaluate the effectiveness of the newly developed shield test device (VMS) by Tongji University in Shanghai for determining the muck state inside EPB shield chambers in fine-grained soils. Muck blockage inside shield chambers frequently results in significant stratum deformation and excessive damage to these machines. The engineering community requires real-time and effective monitoring technology to assess the muck flow state inside EPB shield chambers. Three motion modes of the muck composed of fine-grained soil near the partition have been identified: flow around the central axis, overall static and local adhesion. These modes correspond to the three characteristics of shear plate torque near the shield partition: the periodic fluctuation, the three-stage change of ‘rise-fall-stability’, and a noticeable amplitude difference at the top and lower sides of the chamber. Finally, a preliminarily validated wireless signalling-type shear plate device that can be assembled on the field shield and an actual measurement method was further developed to determine the muck state inside the shield chamber in real-time.

L-Mn dielectronic recombination of cerium ions in a room-temperature EBIT

We explore the dielectronic recombination structures at the electron beam ion trap at Jagiellonian University in Cracow, emited by cerium that is produced by the high-current cathode made of iridium and cerium. Small amounts of these elements evaporate from the cathode and form low-intensity admixtures within the electron-ion plasma in the EBIT. Their presence and specific ionic population can be observed by examining the spectral characteristics resulting from the DR process. Results have been compared with flexible atomic code calculations, working in unresolved transition array mode, providing identification of the charge states. Here we show that this mode provides quick calculations of very complex data with enough reliability for experimental comparison. These observations highlight which DR features and corresponding charge states that should be present in spectra obtained in EBITs with similar cathode specifications.

Vegetation and Dormancy States Identification in Coniferous Plants Based on Hyperspectral Imaging Data

Conifers are a common type of plant used in ornamental horticulture. The prompt diagnosis of the phenological state of coniferous plants using remote sensing is crucial for forecasting the consequences of extreme weather events. This is the first study to identify the “Vegetation” and “Dormancy” states in coniferous plants by analyzing their annual time series of spectral characteristics. The study analyzed Platycladus orientalis, Thuja occidentalis and T. plicata using time series values of 81 vegetation indices and 125 spectral bands. Linear discriminant analysis (LDA) was used to identify “Vegetation” and “Dormancy” states. The model contained three to four independent variables and achieved a high level of correctness (92.3 to 96.1%) and test accuracy (92.1 to 96.0%). The LDA model assigns the highest weight to vegetation indices that are sensitive to photosynthetic pigments, such as the photochemical reflectance index (PRI), normalized PRI (PRI_norm), the ratio of PRI to coloration index 2 (PRI/CI2), and derivative index 2 (D2). The random forest method also diagnoses the “Vegetation” and “Dormancy” states with high accuracy (97.3%). The vegetation indices chlorophyll/carotenoid index (CCI), PRI, PRI_norm and PRI/CI2 contribute the most to the mean decrease accuracy and mean decrease Gini. Diagnosing the phenological state of conifers throughout the annual cycle will allow for the effective planning of management measures in conifer plantations.