Detection Of Abnormalities Research Articles

Acoustic sensing and autoencoder approach for abnormal gas detection in a spent nuclear fuel canister mock-up

Currently, spent nuclear fuel (SNF) from commercial nuclear power plants is stored in stainless-steel canisters for interim dry storage. To provide an inert environment, these canisters are backfilled with helium after vacuum drying. However, the helium environment may be contaminated during extended storage because of the material degradation. For example, the heavier fission gas xenon may be released from the fuel rods into the canister cavity should the fuel cladding be breached. Other gases such as air and water vapor may also be present as a result of leakage caused by chloride-induced stress corrosion cracking on the canister walls or by insufficient vacuum drying. Therefore, monitoring the gas composition can provide critical information about the health of SNF canisters. In this study, noninvasive testing was conducted on a 2/3-scaled SNF canister mock-up using acoustic sensing. Ultrasonic transducers were placed on the exterior surface of the canister to probe the gas composition. A dataset was collected by sealing the canister mock-up and introducing up to 1.53% argon or 1.29% air into the helium background gas. Three methods were used to detect changes in the gas composition: the time-of-flight (TOF) method, the differential method, and the autoencoder method. Results showed that the TOF method had sufficient resolution to detect abnormal gas concentrations of less than 1.0%. The differential method demonstrated a periodic in-phase and out-of-phase behavior between the benchmark (i.e., pure helium) and abnormal (i.e., with argon or air) state signals. The variational autoencoder (VAE) and the Wasserstein autoencoder (WAE) were trained on the benchmark data and were applied directly to the abnormal state data. It was found that both the unsupervised VAE and the WAE were able to distinguish the benchmark and abnormal states of the canister mock-up based on the reconstruction error.

Enhancing Recovery of Structural Health Monitoring Data Using CNN Combined with GRU

Structural health monitoring (SHM) plays a crucial role in ensuring the safety of infrastructure in general, especially critical infrastructure such as bridges. SHM systems allow the real-time monitoring of structural conditions and early detection of abnormalities. This enables managers to make accurate decisions during the operation of the infrastructure. However, for various reasons, data from SHM systems may be interrupted or faulty, leading to serious consequences. This study proposes using a Convolutional Neural Network (CNN) combined with Gated Recurrent Units (GRUs) to recover lost data from accelerometer sensors in SHM systems. CNNs are adept at capturing spatial patterns in data, making them highly effective for recognizing localized features in sensor signals. At the same time, GRUs are designed to model sequential dependencies over time, making the combined architecture particularly suited for time-series data. A dataset collected from a real bridge structure will be used to validate the proposed method. Different cases of data loss are considered to demonstrate the feasibility and potential of the CNN-GRU approach. The results show that the CNN-GRU hybrid network effectively recovers data in both single-channel and multi-channel data loss scenarios.

A Contrastive-Learning-Based Abnormal Electricity Load Detection Method

A Contrastive-Learning-Based Abnormal Electricity Load Detection Method

Fine construction of gene coexpression network analysis using GTOM and RECODE detected a critical module of neuroblastoma stages 4 and 4S.

Stage 4 neuroblastoma (NBL), a solid tumor of childhood, has a poor prognosis. Despite intensive molecular genetic studies, no targetable gene abnormalities have been identified. Stage 4S NBL has a characteristic of spontaneous regression, and elucidation of the mechanistic differences between stages 4 and 4S may improve treatment. Conventional NBL studies have mainly focused on the detection of abnormalities in individual genes and have rarely examined abnormalities in gene networks. While the gene coexpression network is expected to contribute to the detection of network abnormalities, the fragility of the network due to data noise and the extraction of arbitrary topological structures for the high-dimensional network are issues. The present paper concerns the classification method of stages 4 and 4S NBL patients using highly accurate gene coexpression network analysis based on RNA-sequencing data of transcription factors (TFs). In particular, after applying a noise reduction method RECODE, generalized topological overlapping measure (GTOM), which weighs the connections of nodes in the network structure, succeeded in extracting a cluster of TFs that showed high classification performance for stages 4 and 4S. In addition, we investigated how these clusters correspond to clinical information and to TFs which control the normal adrenal tissue and NBL characters. A clustering method is presented for finding intermediate-scale clusters of TFs that give considerable separation performance for distinguishing between stages 4 and 4S. It is suggested that this method is useful as a way to extract factors that contribute to the separation of groups from multiple pieces of information such as gene expression levels.

False-positive XXY results by interphase FISH in cytogenetically normal XX individuals: two cases highlighting the necessity of additional laboratory follow-up.

Interphase fluorescence in situ hybridization (FISH) is commonly used for rapid aneuploidy detection in clinical settings. While FISH-based aneuploidy detection provides rapid results desirable for patient management, it usually only utilizes one probe per chromosome, which may lead to rare false-positive findings. Here we report two interphase FISH results, which were false-positive for XXY in cytogenetically normal XX individuals. Both false-positive cases were due to hybridization of the Y chromosome centromeric probe DYZ3 to the pericentromeric region of chromosome 15. In both cases, chromosomal microarray revealed no detectable Y chromosome material, suggesting the hybridizations of the DYZ3 probe to chromosome 15 likely represent benign heterochromatic variants of no clinical significance. In one case, the DYZ3 hybridization was also identified in the phenotypically unaffected mother, further suggesting this is likely a rare variant of no clinical significance. This report marks the first documentation of hybridization of the DYZ3 probe to another chromosome in cytogenetically normal individuals. Our report has important clinical implications, because DYZ3 is widely used by clinical laboratories for Y chromosome detection. Our findings underscore the necessity of confirming abnormal aneuploidy detection FISH results with additional laboratory methods such as chromosomal microarray analysis.

Explainable early detection of Alzheimer's disease using ROIs and an ensemble of 138 3D vision transformers.

Early detection and accurate diagnosis of brain morphological abnormalities are essential for the effective management and treatment of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Structural magnetic resonance imaging (MRI) is a powerful support tool to aid in disease diagnosis and prediction. In this research study, we present an innovative approach to predict Alzheimer's disease (AD) and mild cognitive impairment (MCI) using MRI data, which integrates regional interest (ROI)-based methodology and deep learning within a comprehensible framework. The proposed method involves dividing the brain into 138 predetermined sections based on anatomical information. Next, we apply three-dimensional vision transformers (3D-ViTs) to each ROI individually, harnessing the power of deep learning. To improve prediction accuracy, we employ a deep belief network (DBN) as an ensemble learning model. Evaluating our approach on the baseline structural MRI dataset obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, and comparing it against five other competing models, we demonstrate its performance across four binary classification tasks and a three-class classification test (AD vs MCI vs CN (Cognitively Normal)). The proposed system outperforms existing models and provides interpretable insights into the brain regions that significantly contribute to solving each classification problem. Our findings align with the existing body of literature and hold promise for guiding future research directions in this domain.

Abstract 4124677: Left ventricular function and myocardial longitudinal strain analysis in patients with Chagas disease: case series and systematic literature review.

Introduction: Chagas disease is a significant cause of tropical disease-related mortality. It presents in acute, indeterminate, and chronic phases, with chronic cardiomyopathy being the most severe form, leading to heart failure, arrhythmias, and sudden cardiac death. Conventional echocardiography often fails to detect early subclinical dysfunction, whereas Global longitudinal strain (GLS) may provide earlier detection of myocardial strain abnormalities. Left ventricular dysfunction is an independent predictor of mortality and GLS is an emerging modality that may aid early detection of cardiac involvement. Objective: This study aims to compare left ventricular function using multiple echocardiographic parameters between patients with indeterminate and chronic forms of Chagas disease and to perform a systematic literature review. Methods: The observational study involved 11 patients (mean age 76.36 years) from a tertiary hospital in Brazil, classified as indeterminate (45%) or chronic (55%). Serological confirmation and detailed echocardiographic evaluations, including GLS, were performed. A systematic literature review was also conducted on GLS in Chagas cardiomyopathy. Results: Patients with chronic Chagas disease showed significantly lower LVEF and GLS compared to those with the indeterminate form. Segmental strain analysis revealed consistent contractility reductions across specific ventricular segments in both groups. Discussion: Chagas cardiomyopathy often leads to dilated cardiomyopathy with significant arrhythmias and heart failure. GLS can detect early myocardial changes even before a significant drop in LVEF, indicating its potential for early diagnosis and better management of Chagas cardiomyopathy. Literature review supports the use of GLS for detecting early cardiac involvement and predicting adverse outcomes. Conclusion: Incorporating GLS with conventional echocardiography offers enhanced early detection of myocardial changes in Chagas disease. While promising, further research is needed to establish the clinical significance and impact of these techniques in routine practice.

An advanced machine vision-based method for abnormal detection of transverse vibrations in ship propulsion shafting

The working environment of ship propulsion shafting is harsh and the force condition is complex, which often produces all-directional vibration. Its working condition will directly affect the navigation performance of the ship. To overcome the limitations of complicated installation route, tedious maintenance process and high cost of traditional contact vibration sensors, an approach of transverse vibration identification model based on machine vision was proposed to realize multi-point vibration displacement sensing and anomaly analysis of shafting. The displacement information of the video signal is extracted by the displacement sensing strip labeling method, and the abnormal state of the ship propulsion shafting is analyzed by the dynamic kernel principal component analysis (DKPCA) algorithm. The experimental results show that the approach can accurately detect the continuous vibration displacement of shafting in the range of 180 r/min, and can work normally under two abnormal conditions: sudden external excitation and continuous uneven external excitation. In addition, this approach can quickly and accurately monitor the motion state of shafting, and realize the perception and recognition of abnormal vibration state of shafting. The research and application of this approach in ship shafting vibration monitoring is of great significance to the development of unmanned and intelligent ships.

Recommendations for the training and practice of fetal cardiology from the Association of European Paediatric Cardiology.

Prenatal detection of cardiac abnormalities has increased significantly over the past few decades, such that fetal cardiology has developed into a sub-specialty of paediatric and congenital cardiology. As this speciality develops further and extends across Europe and more globally, it is important to standardize the requirements for training and subsequent practice, to optimize prenatal diagnosis and perinatal care. In addition to the knowledge and technical skills required to make a correct diagnosis, the counseling of families after diagnosis and the planning of appropriate perinatal management is equally important. The aim of these recommendations is to provide a framework for both basic and advanced training for paediatric cardiologists wishing to practice as fetal cardiologists, as well as highlighting requisites for a fetal cardiology service. All aspects regarding training in fetal cardiology and service provision are addressed including diagnosis, counseling and management.

Building an Impenetrable Vault: Advanced Cybersecurity Strategies for Database Servers

Organizations are relying more on database servers as storage for important information. This makes it imperative for strong measures against cyber-attack to safeguard sensitive data. The study focusses on some advanced ways of protecting database servers from cyber-crime and these are: SQL injection prevention, role-based access control, data encryption techniques, and insider threat mitigation. It also looks into the latest developments in the area such as machine learning based abnormality detection, Zero Trust architecture, and multi-factor authentication. Such practices make it possible to establish a multi-layered defensive mechanism that enhances data protection by considering the outside as well as inside dangers. Besides compliance with regulations, maintaining visibility and patch management is vital so as to remain ahead of changing cyberspace dangers. Therefore, this research presents an array of recommendations to address the entire security landscape for resilient database platforms against sophisticated assaults. The conclusions point out that there is need to amalgamate these practices towards ensuring safety in terms of risks related with contemporary database server setups. Key Words: Database Security, SQL Injection, Role-Based Access Control, Data Encryption, Insider Threat, Zero Trust Architecture.

Fetal growth ultrasound in obese patients for the detection of growth abnormalities.

To examine the impact of maternal obesity on fetal growth abnormalities including fetal growth restriction (FGR) and large for gestational age (LGA) fetuses. Secondary analysis from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (nuMoM2b). The study excluded individuals with pregestational or gestational diabetes, chronic hypertension, and other major maternal medical conditions. Ultrasound assessments were performed at 16 - 21 and 22 - 29 weeks of gestation. Our exposure was the presence of pre-pregnancy obesity. Our primary outcome was rates of fetal growth abnormalities identified by ultrasound, defined as FGR (estimated fetal weight [EFW] or abdominal circumference < 10th percentile) or LGA (EFW > 90 th percentile) among obese compared to non-obese women. A secondary analysis was performed after limiting ultrasound performed from 28-29 weeks. To estimate adjusted relative risks (aRR) with 95% confidence intervals (95%CIs), we used generalized linear models with Poisson distribution and log link using robust error variance, adjusting for the predefined covariates. Of 7,354 participants, 1,443 (19.6%) had pre-pregnancy obesity while 5,911 (80.4%) did not. Pre-pregnancy obesity compared with normal weight was associated with an increased risk of fetal growth abnormalities both at 16-21 weeks (16.0% vs. 13.2%; aRR 1.23; 95%CI 1.06-1.42) and 22-29 weeks (16.0% vs. 12.1%; aRR 1.33; 95%CI 1.14-1.54). Furthermore, pre-pregnancy obesity compared with normal weight was associated with an increased risk of LGA both at 16-21 weeks (12.5% vs. 10.3%; aRR 1.24; 95%CI 1.05-1.47) and 22-29 weeks (10.6% vs. 6.9%; aRR 1.66; 95%CI 1.38-2.01). In a secondary analysis limited to the ultrasound 28-29 weeks, both fetal growth abnormalities and LGA were associated with the presence of obesity. In any of the analyses, pre-pregnancy obesity was not associated with FGR compared to normal weight. Maternal obesity is associated with an increased risk of fetal growth abnormalities and LGA fetuses.

Vision Screening and Detection of Ocular Abnormalities in School Children by Teachers in Jordan.

To evaluate the accuracy of vision screening and detection of ocular abnormalities conducted by teachers in school children in Jordan compared with vision testing by optometrists and ocular disease identification by ophthalmologists. A non-random, purposive sampling strategy where 6-year-old and 15-year-old school children from three schools in Amman, Jordan were included. Twenty-two teachers were trained to conduct visual acuity screening using the 0.2 logMAR line of the distance visual acuity (DVA) chart at 10 feet and near visual acuity (NVA) chart at 16 inches, in addition to detecting ocular abnormalities. An optometrist assessed the visual acuity and conducted objective and subjective refraction, while an ophthalmologist examined the ocular health of all children. A total of 542 children (51% female) were included in the study, of which 47% were 6 years old and 53% were 15 years old. Teacher screening had sensitivities of 76.92% for DVA, 68% for NVA, and 37.50% for abnormality detection. The specificities of DVA, NVA, and ocular abnormalities were 98.22%, 98.48%, and 99.24%, respectively. The positive predictive value for DVA, NVA, and ocular abnormalities were 83.33%, 99.00%, and 60%, respectively. The negative predictive value for each procedure was 98.22% for DVA, 98.48% for NVA, and 98.12% for ocular abnormalities. Teachers were able to conduct vision screening with a high level of accuracy compared to the gold standard of testing by optometrists, which would be useful for the early detection and referral of refractive errors in school children. However, they were unable to detect ocular abnormalities compared with the gold standard for disease identification by ophthalmologists. Further training and monitoring, or different training approaches should be implemented to enable teachers to identify ocular abnormalities at acceptable levels.

SH-YOLO: Small Target High Performance YOLO for Abnormal Behavior Detection in Escalator Scene

SH-YOLO: Small Target High Performance YOLO for Abnormal Behavior Detection in Escalator Scene

Clustering classifier of FRP strengthened concrete beams using superpixels and principal component analysis

Nowadays, the application of Fiber Reinforced Polymer (FRP) materials in concrete structures is more and more extended because of their advantages. However, premature and brittle failure caused by debonding of FRP materials from the concrete surface may be critical and needs to be detected at its earliest stages to avoid possible catastrophic events. Digital image correlation (DIC) has been used in the past for the detection of abnormalities from surface digital images. In this work, an efficient computer-aided diagnosis (CAD) system for damage identification for this type of FRP strengthening is developed. It is based on the combination of cells of strain maps built with a DIC system and an automated clustering classifier optimized with the use of superpixels and principal component analysis. Additionally, preprocessing of the images is also done with some filters to enhance the accuracy of the methodology. The procedure has been validated with experimental results on a progressively damaged FRP externally strengthened beam. Results show that the proposed detection scheme achieves an effective automated classification method for the damage detection of this kind of strengthened specimens. Furthermore, the optimal choice of the subset size, number of superpixels and principal components contributed to the success of the method. Simultaneously, an attempt to quantify damage from the proposed method and its validation with electromechanical impedance method has been developed.

Development of explainable machine intelligence models for heart sound abnormality detection

Development of explainable machine intelligence models for heart sound abnormality detection

Telehealth innovations for cardiovascular disease management

Telehealth innovations and wearable technologies are revolutionizing the management of cardiovascular diseases (CVD), providing patients with more accessible and continuous care. The integration of remote monitoring tools and mobile health applications allows for real-time tracking of vital signs, such as heart rate, blood pressure, and oxygen levels, enabling early detection of abnormalities and timely medical interventions. These technologies empower patients to actively participate in their own health management, encouraging adherence to prescribed lifestyle changes, such as increased physical activity and improved dietary habits, which are essential for preventing and controlling CVD. Wearable devices, such as smartwatches and fitness trackers, play a critical role in the continuous monitoring of cardiovascular health. Combined with telehealth platforms, they offer personalized insights and facilitate communication between patients and healthcare providers, enhancing care coordination. This leads to improved patient outcomes, particularly in managing chronic conditions like hypertension and heart failure, where early intervention is key to preventing complications. Additionally, telehealth solutions reduce the need for frequent in-person visits, cutting healthcare costs and making care more convenient and accessible for patients in remote or underserved areas. As telehealth and wearable technologies continue to advance, they hold the potential to further transform cardiovascular care, contributing to more efficient healthcare delivery and better management of cardiovascular risk factors.

Revolutionizing Radiology With Artificial Intelligence.

Artificial intelligence (AI) is rapidly transforming the field of radiology, offering significant advancements in diagnostic accuracy, workflow efficiency, and patient care. This article explores AI's impact on various subfields of radiology, emphasizing its potential to improve clinical practices and enhance patient outcomes. AI-driven technologies such as machine learning, deep learning, and natural language processing (NLP) are playing a pivotal role in automating routine tasks, aiding in early disease detection, and supporting clinical decision-making, allowing radiologists to focus on more complex diagnostic challenges. Key applications of AI in radiology include improving image analysis through computer-aided diagnosis (CAD) systems, which enhance the detection of abnormalities in imaging, such as tumors. AI tools have demonstrated high accuracy in analyzing medical images, integrating data from multiple imaging modalities such as CT, MRI, and PET to provide comprehensive diagnostic insights. These advancements facilitate personalized treatment planning and complement radiologists' workflows. However, for AI to be fully integrated into radiology workflows, several challenges must be addressed, including ensuring transparency in how AI algorithms work, protecting patient data, and avoiding biases that could affect diverse populations. Developing explainable AI systems that can clearly show how decisions are made is crucial, as is ensuring AI tools can seamlessly fit into existing radiology systems. Collaboration between radiologists, AI developers, and policymakers, alongside strong ethical guidelines and regulatory oversight, will be key to ensuring AI is implemented safely and effectively in clinical practice. Overall, AI holds tremendous promise in revolutionizing radiology. Through its ability to automate complex tasks, enhance diagnostic capabilities, and streamline workflows, AI has the potential to significantly improve the quality and efficiency of radiology practices. Continued research, development, and collaboration will be crucial in unlocking AI's full potential and addressing the challenges that accompany its adoption.

HADNet: A Novel Lightweight Approach for Abnormal Sound Detection on Highway Based on 1D Convolutional Neural Network and Multi-Head Self-Attention Mechanism

Video surveillance is an effective tool for traffic management and safety, but it may face challenges in extreme weather, low visibility, areas outside the monitoring field of view, or during nighttime conditions. Therefore, abnormal sound detection is used in traffic management and safety as an auxiliary tool to complement video surveillance. In this paper, a novel lightweight method for abnormal sound detection based on 1D CNN and Multi-Head Self-Attention Mechanism on the embedded system is proposed, which is named HADNet. First, 1D CNN is employed for local feature extraction, which minimizes information loss from the audio signal during time-frequency conversion and reduces computational complexity. Second, the proposed block based on Multi-Head Self-Attention Mechanism not only effectively mitigates the issue of disappearing gradients, but also enhances detection accuracy. Finally, the joint loss function is employed to detect abnormal audio. This choice helps address issues related to unbalanced training data and class overlap, thereby improving model performance on imbalanced datasets. The proposed HADNet method was evaluated on the MIVIA Road Events and UrbanSound8K datasets. The results demonstrate that the proposed method for abnormal audio detection on embedded systems achieves high accuracy of 99.6% and an efficient detection time of 0.06 s. This approach proves to be robust and suitable for practical applications in traffic management and safety. By addressing the challenges posed by traditional video surveillance methods, HADNet offers a valuable and complementary solution for enhancing safety measures in diverse traffic conditions.

An analysis of decipherable red blood cell abnormality detection under federated environment leveraging XAI incorporated deep learning

In recent times, automated detection of diseases from pathological images leveraging Machine Learning (ML) models has become fairly common, where the ML models learn detecting the disease by identifying biomarkers from the images. However, such an approach requires the models to be trained on a vast amount of data, and healthcare organizations often tend to limit access due to privacy concerns. Consequently, collecting data for traditional centralized training becomes challenging. These privacy concerns can be handled by Federation Learning (FL), which builds an unbiased global model from local models trained with client data while maintaining the confidentiality of local data. Using FL, this study solves the problem of centralized data collection by detecting deformations in images of Red Blood Cells (RBC) in a decentralized way. To achieve this, RBC data is used to train multiple Deep Learning (DL) models, and among the various DL models, the most efficient one is considered to be used as the global model inside the FL framework. The FL framework works by copying the global model’s weight to the client’s local models and then training the local models in client-specific devices to average the weights of the local model back to the global model. In the averaging process, direct averaging is performed and alongside, weighted averaging is also done to weigh the individual local model’s contribution according to their performance, keeping the FL framework immune to the effects of bad clients and attacks. In the process, the data of the client remains confidential during training, while the global model learns necessary information. The results of the experiments indicate that there is no significant difference in the performance of the FL method and the best-performing DL model, as the best-performing DL model reaches an accuracy of 96% and the FL environment reaches 94%-95%. This study shows that the FL technique, in comparison to the classic DL methodology, can accomplish confidentiality secured RBC deformation classification from RBC images without substantially diminishing the accuracy of the categorization. Finally, the overall validity of the classification results has been verified by employing GradCam driven Explainable AI techniques.

Quantitative detection of macular microvascular abnormalities identified by optical coherence tomography angiography in different hematological diseases

It is now understood that hematological diseases can have detrimental effects on the retina, reducing retinal capillaries, compromising visual function, and potentially causing irreversible visual impairment. Over the years, there has been limited research on macular microvascular abnormalities, such as changes in vessel density and the foveal avascular zone (FAZ) and variations in the severity of these effects across different types of blood disorders. This study aims to quantitatively assess the impact of various hematological disorders on the retina using optical coherence tomography angiography (OCTA). Compared with healthy eyes, patients with different blood diseases exhibited reductions in linear vessel density (LVD), perfusion vessel density (PVD), FAZ area, and FAZ perimeter. Notably, patients with erythrocyte diseases showed more significant abnormalities in LVD and PVD, while patients with lymphocytic diseases demonstrated more pronounced abnormalities in the FAZ area and perimeter. OCTA imaging could potentially reflect changes of the retinal microvascular of patients with hematological diseases and may serve as a valuable tool for distinguishing abnormalities affecting different blood cell lines. This approach offers a novel avenue for assessing, treating, and monitoring blood disorders.