Framework For Identification Research Articles

Spatial domains identification in spatial transcriptomics using modality-aware and subspace-enhanced graph contrastive learning

Spatial transcriptomics (ST) technologies have emerged as an effective tool to identify the spatial architecture of tissues, facilitating a comprehensive understanding of organ function and the tissue microenvironment. Spatial domain identification is the first and most critical step in ST data analysis, which requires thoughtful utilization of tissue microenvironment and morphological priors. Here, we propose a graph contrastive learning framework, GRAS4T, which combines contrastive learning and a subspace analysis model to accurately distinguish different spatial domains by capturing the tissue microenvironment through self-expressiveness of spots within the same domain. To uncover the pertinent features for spatial domain identification, GRAS4T employs a graph augmentation based on histological image priors, preserving structural information crucial for the clustering task. Experimental results on eight ST datasets from five different platforms show that GRAS4T outperforms five state-of-the-art competing methods. Significantly, GRAS4T excels at separating distinct tissue structures and unveiling more detailed spatial domains. GRAS4T combines the advantages of subspace analysis and graph representation learning with extensibility, making it an ideal framework for ST domain identification.

An interpretation-based convolution neural network framework for geophysical data fusion and aquifer structure identification

Identification of 3D realistic aquifer structures is essential for predicting physicochemical processes in groundwater systems. However, the characterization of highly heterogeneous aquifers remains challenging because it relies on the effective fusion of multiple geophysical data sources having wide areal coverage, as well as downhole geophysical data featuring high resolution. This study establishes a novel 3D convolutional neural network model to generate aquifer structure from 3D seismic data, constrained by sparse downhole sonic and lithology logs. In the model, the data fusion procedure is designed to follow the logics of conventional manual interpretation of multiple geophysical data, and to address the 3D spatial relationships between geophysical data and lithology. The method is implemented in a typical fluvial aquifer featuring coarse paleovalley sediments (sandstone) embedded in the tight surrounding rocks (claystone), in order to identify channelized sandstone from low-permeability claystone. It is confirmed that the proposed model reliably generates 3D aquifer structures based on seismic amplitudes, downhole sonic and lithology logs. The method is compared to traditional machine learning models that focus on 1D conversion from geophysical attributes to lithology. The results show that the newly-developed model performs more robustly and accurately because the use of 3D convolution allows considering the relationships between seismic amplitude, sonic velocity and lithology in both vertical and horizontal directions. Moreover, the inclusion of sonic logs constraint in the model, following the logics of manual seismic data interpretation, significantly improves the model accuracy. The method can find broad applications for the characterization of subsurface heterogeneity even featuring non-gaussian permeability distribution like the demonstrated fluvial aquifer.

Unsafe behavior identification on construction sites by combining computer vision and knowledge graph–based reasoning

PurposeWorkers' unsafe behavior is the main cause of construction safety accidents, thereby highlighting the critical importance of behavior-based management. To compensate for the limitations of computer vision in tackling knowledge-intensive issues, semantic-based methods have gained increasing attention in the field of construction safety management. Knowledge graph provides an efficient and visualized method for the identification of various unsafe behaviors.Design/methodology/approachThis study proposes an unsafe behavior identification framework by integrating computer vision and knowledge graph–based reasoning. An enhanced ontology model anchors our framework, with image features from YOLOv5, COCO Panoptic Segmentation and DeepSORT integrated into the graph database, culminating in a structured knowledge graph. An inference module is also developed, enabling automated the extraction of unsafe behavior knowledge through rule-based reasoning.FindingsA case application is implemented to demonstrate the feasibility and effectiveness of the proposed method. Results show that the method can identify various unsafe behaviors from images of construction sites and provide mitigation recommendations for safety managers by automated reasoning, thus supporting on-site safety management and safety education.Originality/valueExisting studies focus on spatial relationships, often neglecting the diversified spatiotemporal information in images. Besides, previous research in construction safety only partially automated knowledge graph construction and reasoning processes. In contrast, this study constructs an enhanced knowledge graph integrating static and dynamic data, coupled with an inference module for fully automated knowledge-based unsafe behavior identification. It can help managers grasp the workers’ behavior dynamics and timely implement measures to correct violations.

Identify devices and events from non-IP heterogeneous IoT network traffic

In recent years, notable advancements have been achieved in the realm of identifying IP-based Internet of Things (IoT) devices and events. Nevertheless, the majority of methods rely on extracting fingerprints or features from plain text IP-based packets, which limits their ability to accommodate heterogeneous IoT devices such as ZigBee and Z-Wave, and fails to address the challenge of limited traffic samples. To tackle these issues, we propose a novel approach based on IoT communication characteristics and featuring module extensibility. This method is presented to effectively identify IoT devices and events from non-IP heterogeneous IoT network traffic. To shield the differences caused by the heterogeneous IoT protocol, a heterogeneous sample extraction platform with an extensible structure is created to extract raw sequence samples from ZigBee and Z-Wave traffic, with potential for expansion to other protocols. To address the challenges arising from the scarcity of samples, a sample identification framework based on IoT communication characteristics is devised to create synthetic samples from the raw sequence samples, enabling concurrent processing of the raw and synthetic samples using an identification model featuring two separate sequence networks. Comparative assessments of our method against baseline sequence models and the latest techniques demonstrate the advantages of our approach in identifying non-IP heterogeneous IoT traffic. The experimental results indicate that our method achieves an average accuracy improvement of 29.7% compared to baseline models using only raw samples. Furthermore, our method shows improvements of 22.1%, 21.5%, and 21.8% in macro precision, macro recall, and macro F1-score, respectively, over the latest method.

Joint torque estimation method of industrial robot by combining error compensation model based on LSTM and iterative weighted parameter identification

It is essential for human-robot interaction to accurate joint torque value estimation of industrial robots without force/torque (F/T) sensors. The most common method to estimate the value of robot joint torque is based on dynamic model parameter identification. However, no matter how elaborate the modeling methods are used, there are always uncertainty errors when robot’s joint rotation direction changes. In this paper, an identification framework is proposed to estimate optimal model parameters, and a deep learning network is proposed to compensate for the uncertainty errors caused by the unmodeled dynamics part. At first, the dynamic parameters are estimated during the Least Squares (LS) identification process considering the noise influence and data outliers, and the nonlinear friction model is unified into the identification framework. Secondly, based on Long-Short Term Memory (LSTM) network, an error compensation model (ECM) is proposed to establish the mapping relationship between the joint motion and the identification errors. Finally, a 6-DOF robot is used for parameter identification and ECM validation. Experimental results show that the proposed identification method is better than the LS method. Compared with the torque value estimated by the identification method, the proposed ECM can compensate for the uncertainty errors and improve the torque estimation accuracy.

Navigating complex riskscapes: the Koudiet Eddraouch GIS substation project in Sub-Saharan Africa

Learning outcomes Risk identification and qualitative assessment are the learning outcomes. Case overview/synopsis The Bharat Bijlee Construction Limited (BBCL) was one of the largest construction companies operating in the power sector in India. After successfully completing a few projects in the Middle East, BBCL decided to expand its presence into African region. The BBCL was awarded a $85m contract for three sub-station projects to modernise Algeria’s power grid system by the “Shariket Karhaba Koudiet Eddraouch Spa”, a state-owned company in charge of power generation, transmission and distribution in Algeria. The project, which is the first of BBCL in Saharan region in Algeria’s, presents many unique situations that company had never encountered before, including sand dunes, severe weather, remote locations, supply chain & logistics, strict contractual deadlines and a high level of construction risk. The project manager for BBCL was sceptic about how well his company would perform under the present project circumstances. How could he better align himself with the client, the various on-site local contractors and the numerous suppliers spread around the world? The case emphasises the identification of various project risks that the project manager might encounter in the project. What do the PESTLE and ASCE frameworks for risk identification each represent, and how are they helpful for the project team in understanding various risks? How should the project’s qualitative risk assessment be conducted? And how can a heat map be a better tool for comprehending the criticality of each risk in the project? Complexity academic level Undergraduate and post graduate courses in project management, civil engineering and architecture domain. Supplementary material Teaching notes are available for educators only. Subject code CSS 2: Built Environment.

Nonintrusive identification of boiling regimes enabled by deep learning based on flow boiling acoustics

Monitoring two-phase cooling systems and identifying corresponding boiling regimes are vital to avoid device failure owing to thermal runaway. Boiling acoustic characteristics can be used to develop a nonintrusive monitoring tool because of its close relation to the bubble behavior. Compared with bubble image-based techniques, acoustic sensing is cheaper and easier to implement. However, the acoustic data available is very limited, especially for flow boiling. Also, real-time strategies are still lacking since the prediction models based on boiling acoustics are usually offline. Additionally, noise interference typically degrades model prediction performance. To promote the implementation of an automated boiling system amidst these challenges, we present a deep learning framework for real-time identification of boiling regimes using acoustic signals of flow boiling. Flow boiling experiments are first performed using HFE7100 under different conditions and then combined with deionized water data from our previous experiments to construct the acoustic dataset. We use the corresponding acoustic spectrograms to train and test a convolutional neural network which shows a prediction accuracy of 99.97 % without noise interference. A transfer learning strategy is further employed to improve the original model performance under noise interference. Finally, we add a decision logic to adapt the CNN model to acoustic data streams for real-time identification of boiling regimes. The CNN model with the decision logic can accurately identify the boiling regimes in real-time by scanning the corresponding spectrograms irrespective of working fluids and conditions. In practice, our strategy can be utilized to initiate the necessary precautions to avoid overheating-induced failure.

A new texture-based labeling framework for hyper-reflective foci identification in retinal optical coherence tomography images

An important abnormality in Optical Coherence Tomography (OCT) images is Hyper-Reflective Foci (HRF). This anomaly can be interpreted as a biomarker of serious retinal diseases such as Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) or the progression of disease from an early stage to a late one. In this paper, a new method is proposed for the identification of HRFs. The new method divides the OCT B-scan into patches and separately verifies each patch to determine whether or not the patch contains an HRF. The procedure of patch verification contains a texture-based framework which assigns appropriate labels according to intensity changes to each column and row. Then, a feature vector is extracted for each patch based on the assigned labels. The feature vectors are utilized in the training step of well-known classifiers like Support Vector Machine (SVM). Then, the classifiers are used to produce the labels for the test OCT images. The new method is evaluated on a public dataset including HRF labels. The experimental results show that the new method is capable of providing outstanding results in terms of speed and accuracy.

Identification of equivalent wind and wave loads for monopile-supported offshore wind turbines in operating condition

Wind and wave loads are critical to monopile-supported offshore wind turbines (OWTs) in determining whether OWTs are in safe conditions to generate electricity. However, these random and complex loads vary with time and location, and are difficult to measure directly. Theoretically, the loads can be identified based on structural responses. Nevertheless, it is challenging due to the complexity of external loads and the existence of harmonic loads generated by rotor rotation. This study proposes a new load identification framework using a limited amount of monitoring data to estimate the equivalent wind and wave loads of OWTs simultaneously. Both loads are modeled as Gaussian processes with exponential covariance function and incorporated into the augmented state-space model. Kalman filter is employed to identify the equivalent loads. The effectiveness and accuracy of the proposed method were validated based on a numerical OWT model and a scaled OWT test model. The results demonstrate that the framework can successfully identify equivalent wind and wave loads of OWTs in both parked and operating conditions. The probability distribution of identified load data is consistent with actual load data, with an error of less than 9.1 %. The proposed framework can find vast applications in operational management for OWTs.

A Scoring Framework for the Unsupervised Identification of the Relevance of Metrics in Cell Degradation Clusters

A Scoring Framework for the Unsupervised Identification of the Relevance of Metrics in Cell Degradation Clusters

Experimental identification of longitudinal and vertical lift aerodynamic admittance functions of thin sections

Previous studies of two-dimensional (2D) aerodynamic admittance function (AAF) identification methods have basically ignored the effects of different turbulence components, which may lead to unpredictable errors for practical engineering applications. This paper presents pressure measurement experiments on thin sections with a geometric ratio of 1:50 in various turbulent fields. Based on the three-dimensional (3D) two-wavenumber theoretical analytical framework, the two-wavenumber coherence function is obtained by fitting the measured spanwise root-coherence function at the effective spacing with an empirical model. A new method is proposed, assuming that the ratio relationship between the lift AAFs due to longitudinal and vertical turbulence components of the measured model section satisfies its corresponding theoretical solution ratio relationship. The 2D lift AAFs of the airfoil section induced by different turbulence components are identified following the proposed identification framework. Consistent with the previous research, force correlation is significantly larger than turbulence correlation, and the traditional 3D one-wavenumber AAFs obtained are different in different turbulent fields. The separated 2D lift AAFs demonstrate that the contributions of u- and w- turbulence to the buffeting lift force are significantly different, and are more precise, according to the ratio between the Horlock function and Sears function. Then, the method is successfully extended to estimate the AAFs with respect to the u- and w-turbulence components of a streamlined box girder section, further validating the applicability of the identification method to the streamlined box girder. Furthermore, comparing the separated 2D lift AAFs under different turbulence fields reveals their independence from turbulence characteristics.

Unified External Stakeholder Engagement and Requirements Strategy

Understanding stakeholder needs is essential for project success, as stakeholder importance varies across projects. This study proposes a framework for early stakeholder identification and continuous engagement throughout the project lifecycle. The framework addresses common organizational failures in stakeholder communication that lead to project delays and cancellations. By classifying stakeholders by influence and interest, establishing clear communication channels, and implementing regular feedback loops, the framework ensures effective stakeholder involvement. This approach allows for necessary project adjustments and builds long-term relationships, validated by a survey of IT professionals. Engaging stakeholders strategically at all stages minimizes misunderstandings and project risks, contributing to better project management and lifecycle outcomes.

Timeliness-aware rumor sources identification in community-structured dynamic online social networks

Identifying rumor sources in online social networks (OSNs) plays a crucial role in controlling the spread of rumors and mitigating their damage. However, the community structure of OSNs and timeliness of rumors increase the complexity of accurately characterizing rumor-spreading behavior, making it extremely challenging to identify the source of rumor in OSNs. Conventional studies on rumor source identification often overlook the community structure of OSNs and timeliness of rumors. This paper proposes a rumor sources identification framework that take these two aspects into consideration. First, we transfer the snapshots of a dynamic OSN into a community-structured dynamic OSN through 4 meticulously designed phases. Second, instead of considering a constant influence of rumors in traditional techniques, we introduce a time-varying dynamic propagation parameter to quantify the timeliness of rumor, and apply the propagation parameter to establish a microscopic rumor spreading model. This process addresses the issue of modeling the timeliness of rumor. Third, we adopt sensor-based observation techniques to collect the propagation information of rumor, and a hybrid sensor deployment strategy is designed to improve the efficiency of information collection. Fourth, we propose an algorithm for identifying single and multiple rumor sources in community-structured dynamic OSN, this algorithm initiates with the utilization of some criteria to analyze the gathered propagation information and estimate the suspicious communities that harbor the source of rumors. Subsequently, it applies maximum likelihood estimation method within each community to determine the ultimate source of the rumor. Experimental results on real-world and synthetic networks indicate that our method can accurately identify the real rumor sources. To the best of our knowledge, the proposed method is the first that can be used to identify rumor sources in community-structured dynamic OSNs.

Graph domain adaptation-based framework for gene expression enhancement and cell type identification in large-scale spatially resolved transcriptomics.

Spatially resolved transcriptomics (SRT) technologies facilitate gene expression profiling with spatial resolution in a naïve state. Nevertheless, current SRT technologies exhibit limitations, manifesting as either low transcript detection sensitivity or restricted gene throughput. These constraints result in diminished precision and coverage in gene measurement. In response, we introduce SpaGDA, a sophisticated deep learning-based graph domain adaptation framework for both scenarios of gene expression imputation and cell type identification in spatially resolved transcriptomics data by impartially transferring knowledge from reference scRNA-seq data. Systematic benchmarking analyses across several SRT datasets generated from different technologies have demonstrated SpaGDA's superior effectiveness compared to state-of-the-art methods in both scenarios. Further applied to three SRT datasets of different biological contexts, SpaGDA not only better recovers the well-established knowledge sourced from public atlases and existing scientific literature but also yields a more informative spatial expression pattern of genes. Together, these results demonstrate that SpaGDA can be used to overcome the challenges of current SRT data and provide more accurate insights into biological processes or disease development. The SpaGDA is available in https://github.com/shenrb/SpaGDA.

Cross-contamination Risk Assessment using FMEA tool

In pharmaceutical manufacturing facilities, products with different therapeutic properties and potencies are manufactured in shared spaces, which poses a potential risk for cross-contamination. During the manufacturing process, cross-contamination may occur due to the uncontrolled release of dust, gases/vapors, mix-ups with other materials, residues on equipment, and contamination from operator’s clothing. By implementing adequate facility design and manufacturing operations aligned with Quality Risk Management principles, the risk of cross-contamination can be controlled. If a shared facility is used for manufacturing pharmaceutical products, strategic controls for identifying and managing the risks of cross-contamination should be in place. This article provides a structured and systematic framework for the risk-based identification of the worst-case product and manufacturing process among the products manufactured in the solid oral facility, addressing the high risk of cross-contamination. Keywords: Cross-contamination, Contamination, Risk Assessment, Formal risk assessment, Quality Risk Management (QRM), Failure Mode Effect Analysis (FMEA), Oral Solid Dosage (OSD), Worst case, Contamination Control Strategies (CCS)

A graph self-supervised residual learning framework for domain identification and data integration of spatial transcriptomics

Spatial transcriptomics (ST) technologies allow for comprehensive characterization of gene expression patterns in the context of tissue microenvironment. However, accurately identifying domains with spatial coherence in both gene expression and histology in situ and effectively integrating data from multi-sample remains challenging. Here, we propose ResST, a graph self-supervised residual learning model based on graph neural network and Margin Disparity Discrepancy (MDD) theory. ResST aggregates gene expression, biological effects, spatial location, and morphological information to capture nonlinear relationships between a cell and surrounding cells for spatial domain identification. Also, ResST integrates multiple ST datasets and aligns latent embeddings based on MDD theory for correcting batch effects. Results show that ResST identifies continuous spatial domains at a finer scale in ten ST datasets acquired with different technologies. Moreover, ResST efficiently integrated data from multiple tissue sections vertically or horizontally while correcting batch effects. Overall, ResST demonstrates exceptional performance in analyzing ST datasets.

Association between depression symptom severity and total cost of care: Findings from a large, 2-year, claims-based, retrospective population health study

BackgroundDespite the substantial impact of depression on individuals and healthcare utilization, little is known about the specific relationship between depression severity and total cost of care (TCC). This study evaluates the association between depression symptom severity and TCC and how changes in severity affect TCC. MethodsThe analysis was conducted using insurance claims data and data from electronic health records between January 1, 2019 and December 31, 2020. Inclusion criteria comprised insured individuals with coverage during 2019 or 2020, aged one year or older, and identified as having depression in at least one year of the study. Depression symptom severity was assessed using the screening Identification and Stratification (IDS) framework and data available to the research team. The main outcome was TCC per member per month (PMPM) evaluated across the two-year period. ResultsAcross 2019 and 2020, 744,854 members met inclusion criteria. A total of 369,460 members were studied across both years. Greater depression symptom severity was associated with higher TCC across both years. Unchanged severity was associated with limited change in TCC from 2019 to 2020. Decrease in depression symptoms was associated with an average $41 reduction in PMPM spend, whereas increase in depression symptom severity was associated with an average $608 increase. LimitationsLimitations include fragmented data, retrospective design that limits causality, and the IDS framework design. ConclusionChanges in depression symptom severity were significantly associated with changes in TCC. Findings reveal financial and clinical opportunities associated with early identification and targeted management of depression.

Deep Learning-Based Obesity Identification System for Young Adults Using Smartphone Inertial Measurements.

Obesity recognition in adolescents is a growing concern. This study presents a deep learning-based obesity identification framework that integrates smartphone inertial measurements with deep learning models to address this issue. Utilizing data from accelerometers, gyroscopes, and rotation vectors collected via a mobile health application, we analyzed gait patterns for obesity indicators. Our framework employs three deep learning models: convolutional neural networks (CNNs), long-short-term memory network (LSTM), and a hybrid CNN-LSTM model. Trained on data from 138 subjects, including both normal and obese individuals, and tested on an additional 35 subjects, the hybrid model achieved the highest accuracy of 97%, followed by the LSTM model at 96.31% and the CNN model at 95.81%. Despite the promising outcomes, the study has limitations, such as a small sample and the exclusion of individuals with distorted gait. In future work, we aim to develop more generalized models that accommodate a broader range of gait patterns, including those with medical conditions.

Teaching experience for process identification using first‐order‐plus‐time‐delay models

AbstractThis paper introduces an instructional framework for process identification, combining theoretical concepts with practical laboratory exercises, focusing particularly on the identification of first‐order‐plus‐time‐delay models. Our methodology emphasizes guiding students through the various stages involved in the system identification process, namely mastering techniques, such as data acquisition and preprocessing, identification and validation stages, and method comparison. The laboratory assignment is structured into three distinct stages: an initial prelab task working with simulated data, the hands‐on work with laboratory equipment, and the assignment report writing and oral presentation. The assessment of students' learning outcomes is conducted using a detailed rubric. Feedback from a focus group interview indicates that the majority of students appreciated the well‐balanced content, highlighting a strong link between theoretical concepts and practical application.

Machine learning based damage identification in SiC/SiC composites from acoustic emissions using autoencoders

Developing the ability to leverage machine learning (ML) to identify damage mechanisms in heterogeneous materials from their acoustic emissions (AE) has wide-reaching ramifications for multi-modal experimentation. It would allow researchers to augment damage triangulation, lifetime prediction, and high-resolution optical studies with complementary mechanism-informed data streams. However, developing this capability hinges on the collection of ground truth acoustic libraries from damage in realistic geometries. Due to time and monetary considerations, there is a dearth of ground truth libraries which can be used to robustly characterize ML mechanism identification frameworks. Addressing this gap, we present a multi-modal acoustic emission and x-ray computed tomography study where AE is gathered, and subsequently labeled, from SiC/SiC unidirectional composites under monotonic tension. This library is used to demonstrate that acoustic signals from early fiber breaks are obscured by matrix cracking. A first-order micromechanical model is used to explain the origin of this obscuring effect, and identify fundamental limitations of unsupervised frameworks. An autoencoder-based anomaly detector approach is used for the first time to overcome these limitations, additionally demonstrating that the frequency distribution of fiber break acoustic signals is narrow. Implications of these findings for enhanced multi-modal testing and online health monitoring are discussed, and strategies for implementation of supervised damage mechanism identification frameworks are proposed.