Search Results (281)

As a framework for understanding the structural factors that affect health, the social determinants of health (SDoH) have particular significance during the developmental stage of adolescence. When the global coronavirus pandemic (COVID-19) began, public health measures (PHMs) implemented to curb its spread shifted adolescents’ daily lives and routines, initiating changes to their mental health. The purpose of this study was to apply the SDoH to investigating the impacts of the pandemic-related PHMs on the mental health of adolescents in Canada. Using a youth engagement approach, interviews were conducted with 33 adolescents aged 14–19 years from two sites in Alberta, Canada. Participants shared their experiences of adjusting to the PHMs and how these shaped their mental health. Findings indicate that PHMs particularly affected the social determinants of education, access to health services, employment and income security, and social support amongst adolescents as online schooling, loss of connection with peers, income instability, and limited health services affected their mental health. Most commonly, adolescents expressed feeling greater anxiety, depression, or loneliness as the SDoH shifted with the PHMs. As we continue to understand the mental health impacts of the pandemic, the SDoH framework can be used to identify salient social determinants and evaluate these determinants post-pandemic. This study draws attention to the need for policies and programs that protect access to key SDoH at such a critical life stage as adolescence and promote their mental health resilience in shifting SDoH contexts. Full article

This study presents an advanced data augmentation framework to enhance fault diagnostics in industrial centrifugal pumps using vibration data. The proposed framework addresses the challenge of insufficient defect data in industrial settings by integrating traditional augmentation techniques, such as Gaussian noise (GN) and signal stretching (SS), with advanced models, including Long Short-Term Memory (LSTM) networks, Autoencoders (AE), and Generative Adversarial Networks (GANs). Our approach significantly improves the robustness and accuracy of machine learning (ML) models for fault detection and classification. Key findings demonstrate a marked reduction in false positives and a substantial increase in fault detection rates, particularly in complex operational scenarios where traditional statistical methods may fall short. The experimental results underscore the effectiveness of combining these augmentation techniques, achieving up to a 30% improvement in fault detection accuracy and a 25% reduction in false positives compared to baseline models. These improvements highlight the practical value of the proposed framework in ensuring reliable operation and the predictive maintenance of centrifugal pumps in diverse industrial environments. Full article

In industrial system health management, prognostics play a crucial role in ensuring safety and enhancing system availability. While the data-driven approach is the most common for this purpose, they often face challenges due to insufficient training data. This study delves into the prognostic capabilities of four methods under the conditions of limited training datasets. The methods evaluated include two neural network-based approaches, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks, and two similarity-based methods, Trajectory Similarity-Based Prediction (TSBP) and Data Augmentation Prognostics (DAPROG), with the last being a novel contribution from the authors. The performance of these algorithms is compared using the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) datasets, which are made by simulation of turbofan engine performance degradation. To simulate real-world scenarios of data deficiency, a small fraction of the training datasets from the original dataset is chosen at random for the training, and a comprehensive assessment is conducted for each method in terms of remaining useful life prediction. The results of our study indicate that, while the Convolutional Neural Network (CNN) model generally outperforms others in terms of overall accuracy, Data Augmentation Prognostics (DAPROG) shows comparable performance in the small training dataset, being particularly effective within the range of 10% to 30%. Data Augmentation Prognostics (DAPROG) also exhibits lower variance in its predictions, suggesting a more consistent performance. This is worth highlighting, given the typical challenges associated with artificial neural network methods, such as inherent randomness, non-intuitive decision-making processes, and the complexities involved in developing optimal models. Full article

The inter-shaft bearing is the core component of a high-pressure rotor support system of a high-thrust aero engine. One of the most challenging tasks for a PHM is monitoring its working condition. However, considering that in the bearing rotor system of a high-thrust aero engine bearings are prone to wear failure due to unbalanced or misaligned faults of the rotor system, especially in harsh environments, such as those at high operating loads and high rotation speeds, bearing wear can easily evolve into serious faults. Compared with aero engine fault diagnosis and RUL prediction, relatively little research has been conducted on bearing condition monitoring. In addition, considering how to evaluate future performance states with limited time series data is a key problem. At the same time, the current deep neural network model has the technical challenge of poor interpretability. In order to fill the above gaps, we developed a new framework of a residual space–time feature fusion focusing module named RTCA-Net, which focuses on solving the key problem. It is difficult to accurately monitor the wear state of aero engine inter-shaft bearings under special working conditions in practical engineering. Specifically, firstly, a residual space–time structure module was innovatively designed to capture the characteristic information of the metal dust signal effectively. Secondly, a feature-focusing module was designed. By adjusting the change in the weight coefficient during training, the RTCA-Net framework can select the more useful information for monitoring the wear condition of inter-shaft bearings. Finally, the experimental dataset of metal debris was verified and compared with seven other methods, such as the RTC-Net. The results showed that the proposed RTCA-Net framework has good generalization, superiority, and credibility. Full article

Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas (CRISPR-associated proteins) systems provide acquired heritable protection to bacteria and archaea against selfish DNA elements, such as viruses. These systems must be tightly regulated because they can capture DNA fragments from foreign selfish elements, and also occasionally from self-chromosomes, resulting in autoimmunity. Most known species from the halophilic archaeal genus Haloferax contain type I-B CRISPR-Cas systems, and the strongest hotspot for self-spacer acquisition by H. mediterranei was a locus that contained a putative transposable element, as well as the gene HFX_2341, which was a very frequent target for self-targeting spacers. To test whether this gene is CRISPR-associated, we investigated it using bioinformatics, deletion, over-expression, and comparative transcriptomics. We show that HFX_2341 is a global transcriptional regulator that can repress diverse genes, since its deletion results in significantly higher expression of multiple genes, especially those involved in nutrient transport. When over-expressed, HFX_2341 strongly repressed the transcript production of all cas genes tested, both those involved in spacer acquisition (cas1, 2 and 4) and those required for destroying selfish genetic elements (cas3 and 5–8). Considering that HFX_2341 is highly conserved in haloarchaea, with homologs that are present in species that do not encode the CRISPR-Cas system, we conclude that it is a global regulator that is also involved in cas gene regulation, either directly or indirectly. Full article

Accurate recognition of tool state is important for maximizing tool life. However, the tool sensor data collected in real-life scenarios has unbalanced characteristics. Additionally, although graph neural networks (GNNs) show excellent performance in feature extraction in the spatial dimension of data, it is difficult to extract features in the temporal dimension efficiently. Therefore, we propose a tool state recognition method based on the Pruned Optimized Graph Neural Network-Gated Recurrent Unit (POGNN-GRU) under unbalanced data. Firstly, design the Improved-Majority Weighted Minority Oversampling Technique (IMWMOTE) by introducing an adaptive noise removal strategy and improving the MWMOTE to alleviate the unbalanced problem of data. Subsequently, propose a POG graph data construction method based on a multi-scale multi-metric basis and a Gaussian kernel weight function to solve the problem of one-sided description of graph data under a single metric basis. Then, construct the POGNN-GRU model to deeply mine the spatial and temporal features of the data to better identify the state of the tool. Finally, validation and ablation experiments on the PHM 2010 and HMoTP datasets show that the proposed method outperforms the other models in terms of identification, and the highest accuracy improves by 1.62% and 1.86% compared with the corresponding optimal baseline model. Full article

Digital Twins (DT) play a key role in Industry 4.0 applications, and the technology is in the process of being mature. Since its conceptualisation, it has been heavily contextualised and often misinterpreted as being merely a virtual model. Thus, it is crucial to define it clearly and have a deeper understanding of its architecture, workflow, and implementation scales. This paper reviews the notion of a Digital Twin represented in the literature and analyses different kinds of descriptions, including several definitions and architectural models. A new fit-for-all definition is proposed which describes the underlying technology without being context-specific and also overcomes the pitfalls of the existing generalised definitions. In addition, the existing three-dimensional and five-dimensional models of the DT architecture and their characteristic features are analysed. A new simplified two-branched model of DT is introduced, which retains a clear separation between the real and virtual spaces and outlines the latter based on the two key modelling approaches. This model is then extended for condition monitoring of electronic components and systems, and a hybrid approach to Prognostics and Health Management (PHM) is further elaborated on. The proposed framework, enabled by the two-branched Digital Twin model, combines the physics-of-degradation and data-driven approaches and empowers the next generation of reliability assessment methods. Finally, the benefits, challenges, and outlook of the proposed approach are also discussed. Full article

Tool condition monitoring technology is an indispensable part of intelligent manufacturing. Most current research focuses on complex signal processing techniques or advanced deep learning algorithms to improve prediction performance without fully leveraging the end-to-end advantages of deep learning. The challenge lies in transforming multi-sensor raw data into input data suitable for direct model feeding, all while minimizing data scale and preserving sufficient temporal interpretation of tool wear. However, there is no clear reference standard for this so far. In light of this, this paper innovatively explores the processing methods that transform raw data into input data for deep learning models, a process known as an input paradigm. This paper introduces three new input paradigms: the downsampling paradigm, the periodic paradigm, and the subsequence paradigm. Then an improved hybrid model that combines a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) was employed to validate the model’s performance. The subsequence paradigm demonstrated considerable superiority in prediction results based on the PHM2010 dataset, as the newly generated time series maintained the integrity of the raw data. Further investigation revealed that, with 120 subsequences and the temporal indicator being the maximum value, the model’s mean absolute error (MAE) and root mean square error (RMSE) were the lowest after threefold cross-validation, outperforming several classical and contemporary methods. The methods explored in this paper provide references for designing input data for deep learning models, helping to enhance the end-to-end potential of deep learning models, and promoting the industrial deployment and practical application of tool condition monitoring systems. Full article

Poly(hydromethylsiloxane) (PHMS) was cross-linked with 1,3,5,7-tetramethyl-1,3,5,7-tetravinylcyclotetrasiloxane (D₄^Vi) in water-in-oil High Internal Phase Emulsions to form macroporous materials known as polyHIPEs. It was shown that in the process of pyrolysis under Ar atmosphere at 520 °C, the obtained polyHIPEs were converted to ceramers with high yields (82.8–88.0 wt.%). Structurally, the obtained ceramers were hybrid ceramics, i.e., they consisted of Si-O framework and preserved organic moieties. Macropores present in the polyHIPE precursors remained in ceramers. Ceramers contained also micro- and mesopores which resulted from the precursor’s mass loss during pyrolysis. Total pore volume and BET specific surface area related to the existence of micro- and mesopores in ceramers depended on the PHMS: D₄^Vi ratio applied in polyHIPE synthesis. The highest total pore volume (0.143 cm³/g) and specific surface area (344 m²/g) were reached after pyrolysis of the precursor prepared with the lowest amount of D₄^Vi as compared to PHMS. The composite materials obtained after deposition of PdO nanoparticles onto ceramers followed by reduction of PdO by H₂ were active and selective catalysts for phenylacetylene hydrogenation to styrene. Full article

The prediction of the health status of critical components is an important influence in making accurate maintenance decisions for rotating equipment. Since vibration signals contain a large amount of fault information, they can more accurately describe the health status of critical components. Therefore, it is widely used in the field of rotating equipment health state prediction. However, there are two major problems in predicting the health status of key components based on vibration signals: (1) The working environment of rotating equipment is harsh, and if only one feature in the time or frequency domain is selected for fault analysis, it will be susceptible to harsh operating environments and cannot completely reflect the fault information. (2) The vibration signals are unlabeled time series data, which are difficult to accurately convert into the health status of key components. In order to solve the above problems, this paper proposes a combined prediction model combining a bidirectional long- and short-term memory network (BiLSTM), a self-organizing neural network (SOM) and particle swarm optimization (PSO). Firstly, the SOM is utilized to fuse the fault characteristics of multiple vibration signals of key components to obtain an indicator (HI) that can reflect the health status of rotating equipment and to also compensate for the vulnerability of single signal characteristics in the time or frequency domain to environmental influences. Secondly, the K-means clustering method is employed to cluster the health indicators and determine the health state, which solves the problem of determining the health of a component from unsupervised vibration signal data which is quite difficult. Finally, the particle swarm optimized BiLSTM model is used to predict the health state of key components and the bearing dataset from the IEEE PHM 2012 Data Challenge verifies the method’s effectiveness and validity. Full article

The Herpotrichiellaceae family is an important group of dematiaceous filamentous fungi, associated with a variety of pathogenic fungal species causing chromoblastomycosis (CBM) and phaeohyphomycosis (PHM), both with polymorphic clinical manifestations and worldwide incidence. Currently, the identification of this family and determination of the causative agent is challenging due to the subjectivity of morphological identification methods, necessitating the use of molecular techniques to complement diagnosis. In this context, genetic sequencing of the Internal Transcribed Spacer (ITS) has become the norm due to a lack of alternative molecular tools for identifying these agents. Therefore, this study aimed to develop PCR-Multiplex methodologies to address this gap. Sequences from the ITS and Large Subunit (LSU) of ribosomal DNA were used, and after manual curation and in vitro analyses, primers were synthesized for the identification of the targets. The primers were optimized and validated in vitro, resulting in two PCR-Multiplex methodologies: one for identifying the Herpotrichiellaceae family and the bantiana clade, and another for determining the species Fonsecaea pedrosoi and Fonsecaea monophora. Ultimately, the assays developed in this study aim to complement other identification approaches for these agents, reducing the need for sequencing, improving the management of these infections, and enhancing the accuracy of epidemiological information. Full article

Remaining useful life (RUL) prediction is widely applied in prognostic and health management (PHM) of turbofan engines. Although some of the existing deep learning-based models for RUL prediction of turbofan engines have achieved satisfactory results, there are still some challenges. For example, the spatial features and importance differences hidden in the raw monitoring data are not sufficiently addressed or highlighted. In this paper, a novel multi-head self-Attention fully convolutional network (MSA-FCN) is proposed for predicting the RUL of turbofan engines. MSA-FCN combines a fully convolutional network and multi-head structure, focusing on the degradation correlation among various components of the engine and extracting spatially characteristic degradation representations. Furthermore, by introducing dual multi-head self-attention modules, MSA-FCN can capture the differential contributions of sensor data and extracted degradation representations to RUL prediction, emphasizing key data and representations. The experimental results on the C-MAPSS dataset demonstrate that, under various operating conditions and failure modes, MSA-FCN can effectively predict the RUL of turbofan engines. Compared with 11 mainstream deep neural networks, MSA-FCN achieves competitive advantages in terms of both accuracy and timeliness for RUL prediction, delivering more accurate and reliable forecasts. Full article

Condition monitoring (CM) is the basis of prognostics and health management (PHM), which is gaining more and more importance in the industrial world. CM, which refers to the tracking of industrial equipment’s state of health during operations, plays, in fact, a significant role in the reliability, safety, and efficiency of industrial operations. This paper proposes a data-driven CM approach based on the autoregressive (AR) modeling of the acquired sensor data and their analysis within frequency subbands. The number and size of the bands are determined with negligible human intervention, analyzing only the time–frequency representation of the signal of interest under normal system operating conditions. In particular, the approach exploits the synchrosqueezing transform to improve the signal energy distribution in the time–frequency plane, defining a multidimensional health indicator built on the basis of the AR power spectral density and the symmetric Itakura–Saito spectral distance. The described health indicator proved capable of detecting changes in the signal spectrum due to the occurrence of faults. After the initial definition of the bands and the calculation of the characteristics of the nominal AR spectrum, the procedure requires no further intervention and can be used for online condition monitoring and fault diagnosis. Since it is based on the comparison of spectra under different operating conditions, its applicability depends neither on the nature of the acquired signal nor on a specific system to be monitored. As an example, the effectiveness of the proposed method was favorably tested using real data available in the Case Western Reserve University (CWRU) Bearing Data Center, a widely known and used benchmark. Full article

Hydatidiform moles, including both complete and partial moles, constitute a subset of gestational trophoblastic diseases characterized by abnormal fertilization resulting in villous hydrops and trophoblastic hyperplasia with or without embryonic development. This involves chromosomal abnormalities, where one or two sperms fertilize an empty oocyte (complete hydatidiform mole (CHM); mostly 46,XX) or two sperms fertilize one oocyte (partial hydatidiform mole (PHM); mostly 69,XXY). Notably, recurrent occurrences are associated with abnormal genomic imprinting of maternal effect genes such as NLRP7 (chromosome 19q13.4) and KHDC3L (chromosome 6q1). Ongoing efforts to enhance identification methods have led to the identification of growth-specific markers, including p57 (cyclin-dependent kinase inhibitor 1C; CDKN1C), which shows intact nuclear expression in the villous cytotrophoblast and villous stromal cells in PHMs and loss of expression in CHMs. Treatment of hydatidiform moles includes dilation and curettage for uterine evacuation of the molar pregnancy followed by surveillance of human chorionic gonadotropin (HCG) levels to confirm disease resolution and rule out the development of any gestational trophoblastic neoplasia. In this review, we provide a synopsis of the existing literature on hydatidiform moles, their diagnosis, histopathologic features, and management. Full article

With the rapid development of science and technology, the integration and complexity of aerospace vehicles, weaponry, and large-scale chemical equipment are becoming higher and higher. PHM plays an important role in realizing reductions in equipment loss due to failures in many fields. In order to systematically sort through the research history of PHM and deeply analyze the development status of AR and DT technologies in the field of PHM, to clarify the current technical challenges and future development directions and to provide valuable references and insights for researchers, engineers, and decision-makers in the related fields, this paper summarizes the development of PHM in the engineering field. This paper summarizes the development of PHM in the field of engineering, from the initial PHM used in aerospace to the current PHM systems supported by various advanced technologies; analyzes the advantages and shortcomings of the digital twin and augmented reality technologies used for PHM; and organizes and summarizes the future directions and future research focuses of PHM research based on the existing technologies (mainly digital twins and augmented reality). After systematic research and study, we found that the integration of augmented reality and digital twin technologies will provide superb simulation capabilities and immersive operation and bring new challenges and opportunities. Therefore, it is also imperative to address the challenges and limitations that hinder the seamless integration of the new technologies. Full article